{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOMmLRT0MQlOUbFnkNPnoH2",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aleksandrkaplun/WineQuality/blob/main/WineClassifPT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "KzK77Q6fLPFe"
      },
      "outputs": [],
      "source": [
        "# Import Libraries\n",
        "from sklearn.model_selection import train_test_split\n",
        "from torch.utils.data import TensorDataset, DataLoader\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import scipy.stats as stats\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Matplotlib settings\n",
        "plt.rcParams.update({'font.size': 12})\n",
        "plotSizeGen = lambda n: (16, n*9)\n",
        "singlePlot = plotSizeGen(1)"
      ],
      "metadata": {
        "id": "VzC9u__oM9mn"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('winequality-red (1).csv', sep=';')\n",
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "IBHSc7VfNgF6",
        "outputId": "40b6fe95-8d7c-4a98-cb53-4a3d27619fd3"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0            7.4              0.70         0.00             1.9      0.076   \n",
              "1            7.8              0.88         0.00             2.6      0.098   \n",
              "2            7.8              0.76         0.04             2.3      0.092   \n",
              "3           11.2              0.28         0.56             1.9      0.075   \n",
              "4            7.4              0.70         0.00             1.9      0.076   \n",
              "\n",
              "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
              "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
              "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
              "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "\n",
              "   alcohol  quality  \n",
              "0      9.4        5  \n",
              "1      9.8        5  \n",
              "2      9.8        5  \n",
              "3      9.8        6  \n",
              "4      9.4        5  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-289995d0-e26b-453b-8414-7467595fd74c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-289995d0-e26b-453b-8414-7467595fd74c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-289995d0-e26b-453b-8414-7467595fd74c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-289995d0-e26b-453b-8414-7467595fd74c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9fa494c4-b1fd-49a4-91b8-e173976604d9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9fa494c4-b1fd-49a4-91b8-e173976604d9')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9fa494c4-b1fd-49a4-91b8-e173976604d9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataset",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 1599,\n  \"fields\": [\n    {\n      \"column\": \"fixed acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7410963181276953,\n        \"min\": 4.6,\n        \"max\": 15.9,\n        \"samples\": [\n          5.3,\n          12.7,\n          12.6\n        ],\n        \"num_unique_values\": 96,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volatile acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17905970415353537,\n        \"min\": 0.12,\n        \"max\": 1.58,\n        \"samples\": [\n          1.025,\n          0.4,\n          0.87\n        ],\n        \"num_unique_values\": 143,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"citric acid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19480113740531857,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"samples\": [\n          0.37,\n          0.0,\n          0.09\n        ],\n        \"num_unique_values\": 80,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"residual sugar\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4099280595072798,\n        \"min\": 0.9,\n        \"max\": 15.5,\n        \"samples\": [\n          11.0,\n          3.0,\n          15.5\n        ],\n        \"num_unique_values\": 91,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chlorides\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0470653020100901,\n        \"min\": 0.012,\n        \"max\": 0.611,\n        \"samples\": [\n          0.096,\n          0.343,\n          0.159\n        ],\n        \"num_unique_values\": 153,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"free sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.460156969809725,\n        \"min\": 1.0,\n        \"max\": 72.0,\n        \"samples\": [\n          11.0,\n          9.0,\n          32.0\n        ],\n        \"num_unique_values\": 60,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.895324478299074,\n        \"min\": 6.0,\n        \"max\": 289.0,\n        \"samples\": [\n          68.0,\n          35.0,\n          101.0\n        ],\n        \"num_unique_values\": 144,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"density\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0018873339538425554,\n        \"min\": 0.99007,\n        \"max\": 1.00369,\n        \"samples\": [\n          0.99974,\n          1.0001,\n          0.99471\n        ],\n        \"num_unique_values\": 436,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15438646490354277,\n        \"min\": 2.74,\n        \"max\": 4.01,\n        \"samples\": [\n          3.07,\n          3.0,\n          3.15\n        ],\n        \"num_unique_values\": 89,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sulphates\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16950697959010996,\n        \"min\": 0.33,\n        \"max\": 2.0,\n        \"samples\": [\n          1.07,\n          1.04,\n          1.18\n        ],\n        \"num_unique_values\": 96,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0656675818473946,\n        \"min\": 8.4,\n        \"max\": 14.9,\n        \"samples\": [\n          8.5,\n          9.95,\n          9.4\n        ],\n        \"num_unique_values\": 65,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quality\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 8,\n        \"samples\": [\n          5,\n          6,\n          3\n        ],\n        \"num_unique_values\": 6,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing outliers\n",
        "dataset = dataset[dataset['total sulfur dioxide'] < 200]"
      ],
      "metadata": {
        "id": "tlSWE708PX-W"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Box plot the dataset for visualization\n",
        "\n",
        "fig,ax = plt.subplots(1,1,figsize=singlePlot)\n",
        "\n",
        "ax = sns.boxplot(dataset)\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=45)\n",
        "\n",
        "ax.set_title('Box plot the dataset for visualization')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 667
        },
        "id": "u_oDHzkBP10S",
        "outputId": "f13fb232-bae5-446f-9b4c-58938b0bd8fe"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-8e70efe3d5ed>:6: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x900 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABRUAAANkCAYAAAAp1s0tAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeZjVZf0//ufMMDOAyKi4gCAC7hiFpiZi2KYpKJp7QGpaCmnZXqYkpmnLJ/v0SQL7ZmouuWC5hZlahhJqlBSZe5SymUIMEMvAzPn94W9OTow6RxnOgI/Hdc3lzH2/znte58wM1/ic+77fFYVCoRAAAAAAgDaqLHcDAAAAAMDGRagIAAAAAJREqAgAAAAAlESoCAAAAACURKgIAAAAAJREqAgAAAAAlESoCAAAAACURKgIAAAAAJREqAgAAAAAlESoCACUxXve855UVFSUu41X1a9fv/Tr16/cbazj73//eyoqKnLKKaeUu5WNxk9/+tPstdde2XzzzVNRUZFPf/rT5W7pdVVUVOQ973lPudt4Ta31OGHChFRUVOT+++8vS0//7f77709FRUUmTJhQ7lYAYJMjVASADqqiomKdt9ra2vTr1y8nn3xyHn/88XK32CG80fClo4ea7W1jClveTK8zZszI6NGjs2zZsowbNy7nn39+Dj300PXfJGUhZAeA8ulU7gYAgNd2/vnnF9+vr6/PI488kp/85Ce55ZZb8uCDD2bw4MHlaw46uF/84hcpFAr5yU9+kgMOOKDc7bTZ448/nq5du5a7jZKdddZZOfHEE9O3b99yt5Ik2W+//fL4449n6623LncrALDJESoCQAfX2uqsT37yk7nsssvyv//7v7nqqqs2eE+wsZg/f36SZPvtty9zJ6XZfffdy93CG7L11lt3qACva9euG+1rCQAdne3PALAROuSQQ5IkL7744jpzq1evzje+8Y0MGjQoXbt2Tffu3fPud787N910U4u6n/3sZ6moqMj++++fNWvWtJj7y1/+kq5du2b77bfPP//5z9ft55XnqF199dXZa6+90qVLl2y77bY59dRTs3DhwjY/t6ampkyePDn77rtvunXrls022yz77rtvJk2alKampmLdVVddVdy+/Nvf/rbFNvHX2ibbvF3yt7/9bZKW28xb20b973//O1/4whfSt2/f1NbWZuedd843v/nNFAqFVq//8MMP59hjj03Pnj1TU1OTHXbYIWeccUYx3GqrZcuW5bOf/Wz69OmTzp07Z/fdd8+ll17a4jV4paeeeipf/vKXs88++2SbbbZJbW1tdtxxx5x++umZO3dui9pTTjkl733ve5MkF1xwQYvXoPksvPr6+nz729/O+973vvTp0yc1NTXZZpttMnLkyMyYMaPVHh544IEcccQR6dOnT2pra9OzZ8/sv//+ueCCC9apXbFiRS655JIMHjw4m222Wbp165YhQ4bkpz/9acm9tqb5++PKK69MkvTv37/4uL///e/Fuj/84Q855phjsu222xZfs0984hNZsGDBOtc85ZRTUlFRkb/97W/5/ve/n7e//e3p0qXLa26/X7VqVbbYYotsu+22Wbt2bas148aNS0VFRe68887iWGvfj8uWLcuFF16Yt73tbenevXs233zz7LTTTjnhhBPyhz/8oVj3etvFWzsv9I18vVvT2pmKzUcNvNrbK5/n/Pnz87WvfS1Dhw4t/gxtv/32GTVqVP7617+u87n69++fJLn66qtbXLP5jy2v9Vo8/fTTOemkk9K7d+/i5znppJPy9NNPv+bzmjJlSvbbb7907do1W221VU488cTMmzevza8RAGwqrFQEgI3QvffemyTZZ599Wow3NDTkgx/8YH77299m9913z5lnnpkVK1ZkypQpOeGEEzJr1qxcfPHFSZKjjz46Z555ZiZOnJhzzz033/rWt5K8HPYcf/zxWb16da677rpsu+22be7ru9/9bn71q1/lhBNOyKGHHpoHH3wwV155Ze6///48/PDD2WabbV73Gh/5yEdy/fXXZ4cddsjHPvaxVFRU5Oc//3k+8YlP5MEHH8x1112XJBk8eHDOP//8XHDBBdlxxx1bnKn2WiHPFltskfPPPz9XXXVV/vGPf7TYXv7fQcuaNWvywQ9+MPPnz89hhx2WTp065dZbb82Xv/zlrFq1qsVjk+THP/5xTj/99NTW1mbkyJHZYYcd8vTTT+dHP/pR7rjjjjz00ENt2ha6evXqvP/978/vf//7vOMd78jo0aOzZMmSXHjhhcUw9L/97Gc/y+TJk/Pe9743BxxwQGpqavLYY48VP/fMmTPTu3fvJMlRRx2V5OUg5qCDDmrxejW/Bo8//njOPffcDBs2LCNGjMiWW26Z5557Lrfffnvuuuuu3HHHHS3OJvzlL3+ZESNGpHv37hk5cmR69+6dxYsX5/HHH88PfvCDFq/VkiVL8r73vS+PPvpo9t5775x66qlpamrK3XffnVGjRuWxxx7LRRdd1OZeW9P8/XHrrbfmT3/6U84+++xsscUWSVL875133pljjjkmhUIhxx57bHbcccf84Q9/yKRJk3LbbbflwQcfLIZWr3T22WfngQceyIgRIzJ8+PBUVVW9ah+dO3fOCSeckB/+8Ie56667csQRR7SYX716dW688cZst912r3nWY6FQyKGHHprf/e53GTJkSD72sY+lU6dOmTt3bn7zm9/k3e9+d975zne+6uNfT6lf71Kccsoprf5MPvDAA/n1r3/dYpv3tGnT8o1vfCPvfe97c8wxx6Rbt255+umnM2XKlNx+++2ZPn163vGOdyR5+ed8yZIl+d73vpd3vOMdxe+VJK97LMTvf//7fOADH8iyZcsycuTIDBw4ME888USuvfba3Hbbbbn33nuz7777rvO4H/zgB7n99tszcuTIHHTQQXn44Ydz44035k9/+lNmzZqV2traN/QaAcBGqQAAdEhJCkkK559/fvHtM5/5TOHAAw8sVFRUFA4//PDC0qVLWzzm4osvLiQpHHbYYYU1a9YUx1944YXCjjvuWEhSmD59enF81apVhb322qtQUVFRuOuuuwqFQqFwyimnFJIUvvrVr7a51/PPP7+QpFBdXV344x//2GLu05/+dCFJ4dRTT20xftBBBxX++1eR66+/vpCksNdeexWWLVtWHF++fHnhne98ZyFJ4brrrlvndTrooIPa3Otrff5Xan69DjvssMKKFSuK4y+88EKhrq6uUFdXV2hoaCiOP/nkk4Xq6urCTjvtVJg7d26La917772FysrKwlFHHdWm3r7+9a8XkhSOPvroQmNjY3H8b3/7W2HLLbcsJCmcfPLJLR4zd+7cwqpVq9a51t13312orKwsjB07tsX4b37zm+L3V2uWLFlSePHFF9cZf/755wu9evUq7L777i3Gjz766EKSwqxZs9Z5zH9f5+STTy4kKXzzm99sMb5y5crCBz/4wUJFRUXh0UcfbXOvr6X5c82ZM6fF+LJlywpbbbVVobKysjBt2rQWc9/4xjcKSQoHH3xwq9fafvvtC3/729/a3MPvfve7QpLCMcccs87cTTfdVEhS+OxnP9ti/L+/r//85z8XkrT6PdTY2FhYvHhx8ePXe7123HHHwo477thirNSvd2s9Fgr/+bfgN7/5Taufu9mf/vSnwuabb17YeuutC88880xx/IUXXljn37VCoVCYNWtWYbPNNisceuihLcbnzJnT6s9Ds9Zei6ampsLuu+9eSFK49tprW9TfcMMNhSSF3XbbrcXPXvPz2nzzzQt//vOfWzzmwx/+cCFJ4cYbb3zN5wwAmxrbnwGgg7vggguKb9/97nfz4IMPZo899siHP/zhbL755i1qf/zjH6eioiKXXnppOnX6z4aEbbfdNuPHj0+S/OhHPyqO19bW5sYbb8xmm22Wk046Kf/zP/+Tq666KsOGDctXv/rVknv9yEc+kr322qvF2IQJE1JXV5frr78+q1evfs3H//jHP06SfOMb30i3bt2K45tttlm++c1vrtP/hvB///d/6dKlS/HjbbfdNkceeWTq6+vz5JNPFscnTZqUNWvW5Hvf+15xRWCz97///Rk5cmTuuOOOLFu27HU/55VXXpnKysp861vfSmXlf35d69+/fz71qU+1+pjevXu3ukrqkEMOyZ577pm77777dT/vK9XV1bV6Nl6fPn1y7LHH5oknnshzzz23zvwrX6tmr7zOokWLcu2112afffbJF7/4xRZ1nTt3Lm4tv/7660vqt1S33XZbFi9enBNOOCHvfve7W8x97nOfS79+/XLPPfe0+hy/+MUvtrqC8dUMGTIku+66a+64444sXry4xdzVV1+dJDn55JPbdK3WXt/KyspsueWWbe6nNW/06/1GzJ8/PyNGjMiaNWty2223ZaeddirObbvttuv8u5Yk73jHO/K+970vv/nNb9Y5rqFUv/vd7/LEE09kyJAhGT16dIu5E044IQceeGCefPLJPPjgg+s89lOf+lQGDRrUYuzjH/94kuSRRx55U30BwMbG9mcA6OAKrzi779///ncee+yxfPnLX87o0aPz2GOP5etf/3qSl89be+aZZ9K7d+9Wb0zwvve9L0ny6KOPthjfZZddMnny5IwZMyZf+MIXsvXWW+f6669/zS2dr+aggw5aZ6yuri6DBw/Ob3/72zz++OOvuS3xj3/8YyorK1vdKnnQQQelqqpqnf7bU11dXXbeeed1xnfYYYckyb/+9a/iWPO5c7/97W/z+9//fp3H/POf/0xjY2Oeeuqp19ym2vx13GGHHVqELc3e8573tHpGYaFQyHXXXZerrroqf/rTn/Kvf/0rjY2NxfmamprXeKatmz59er73ve9lxowZ+ec//5mGhoYW8/PmzStu5x49enR+9rOf5V3veldOOOGEvPe9783QoUPTp0+fFo/5/e9/n8bGxlc95645MHr88cdL7rcUf/zjH5P85+filTp16pRhw4bl73//ex599NF1tqzvt99+JX++k08+Oeeee25uuOGGfOITn0iSvPDCC7n77ruz11575e1vf/trPn7gwIEZPHhwfvrTn+Yf//hHjjzyyBx44IHZZ5993tDXtjWlfL3fqOXLl+fwww/PvHnz8tOf/rTVO3L/4he/yOTJkzNz5sy89NJL65xF+dJLL6VXr15vuIfX+to3jz/44IN59NFHM2zYsBZz/33kRNL6vwcA8FYgVASAjchmm22W/fbbLz/72c/Sp0+ffOtb38rYsWOzww47pL6+Pkle9X+2m8eXLFmyztwhhxyS7t27Z+nSpTnuuOPWWWnXVtttt12r4z179kySYo+vpr6+PltttVWrIUmnTp2y9dZbt+nGMetL89l7rfWSpEVot2jRoiTJt7/97de85vLly19zvvk1er3X8r999rOfzf/+7/+mV69e+eAHP5jevXsXV7U1nx9Zip///Oc59thj07lz5xx88MHZaaedstlmm6WysjL3339/fvvb37ZYeXr00UfnzjvvzHe+8538+Mc/zuWXX54keec735lLLrkkBx98cJL/vE6///3vWw1fm73e6/RmvZmfl1f7GryWk046KePHj8/VV19dDBWvu+66rF27tk2rFKuqqvLrX/86X/va1zJlypR86UtfSpJsvvnmOfnkk3PJJZe0WN1bqlK/3m9EY2NjTjjhhDz66KO55JJLcsIJJ6xT873vfS+f/vSns+WWW+bggw9O375907Vr11RUVBTPx3yzfbyZr31r/ya09u8BALwVCBUBYCO0xRZbZLfddssf//jH/PGPf8wOO+yQurq6JHnVOy033822ua5ZoVDISSedlKVLl2brrbfOD3/4w5x44onrrNBpixdeeKHV8eae/vtz/7e6urosXrw4a9asSXV1dYu5tWvX5qWXXkr37t1L7mtDaH5u9fX1b6rH5uu83mv5Sv/85z/zf//3f3nb296W3/3ud+tsH/3vOyq3xfjx41NTU5OZM2dmjz32aDF3xhlntHrDmBEjRmTEiBH597//nYcffjh33nlnJk2alMMPPzyPPvpoBg4cWHx+n/nMZ3LppZeW3Nf68kZ/XpIU7zpeij59+uR973tf7r333jzxxBPZfffdc/XVV6e6ujqjRo1q0zW23HLLfPe73813v/vdPPPMM/ntb3+byy+/PJdddlmWLFmSa665JkmKW+Zf7W7TS5YsWScceyNf71J98pOfzNSpU/Pxj388X/7yl9eZX7t2bSZMmJCePXvmj3/84zqhXyl3oX4tb+ZrDwD8hzMVAWAj1bzVrqmpKcnLK5Z22mmnzJs3L08//fQ69b/5zW+SJHvvvXeL8W9/+9v55S9/mdGjR+fXv/51MeRoXlFWitaCh/r6+syaNSudO3deJ6z4b3vttVeampoybdq0deamTZuWxsbGdfqvrKx8QyuEmrd3r6/VRfvvv3+Sl+9o+2Zsvvnm2XnnnTNv3rw8++yz68zff//964z97W9/S1NTUw455JB1AsW5c+fmb3/72zqPeb3n/8wzz2TgwIHrfM2amppaPWvulTbbbLO8733vy6WXXpqvfOUraWhoyF133ZXk5a3DlZWVJb1O6/trlaR49mdrr+fatWuL/f3399ub0XyH8quvvjqzZs3Kn//85xx22GFtuiv6f9t5551z2mmn5be//W26deuW2267rTjXfL7i888/v87jnnnmmVZXDL+Zr3dbfOc738mkSZNyyCGH5Ac/+EGrNS+99FKWLFmSAw44YJ1Acfny5cVty6/0Rr43Xutrn7z6v5UAQEtCRQDYCN16662ZM2dOqqurW5xJduqpp6ZQKOQLX/hCi//Jfumll3LhhRcWa5o99NBDOffcc7Pzzjtn0qRJGTRoUL773e9m3rx5Ofnkk1uc59gW11xzzTpnHk6YMCH19fX58Ic/3OqNRF6pubdzzjknK1asKI6vWLGiuLLptNNOa/GYHj16tBqevJ4ePXokyXq7+cRZZ52V6urqfOYzn8lTTz21znxDQ0Obg7SPfvSjaWpqype+9KViaJwkc+bMyf/93/+tU9+vX78kyYMPPtji6758+fJ8/OMfb3XF2us9/379+uXpp5/O/Pnzi2OFQiETJkzIX//613Xqp02b1urnaV5x2bVr1yQv34hj9OjRmTlzZi688MJWw6Bnn302c+bMaXOvb8RRRx2VrbbaKj/96U/z0EMPtZj73//938yZMycf+MAH3vQZgq909NFHp3v37rn22mtz1VVXJflP0Ph65syZ02o4/K9//SurV69ucQOX3XffPd27d89tt93W4riAlStXvuqNfkr9epfiZz/7Wb74xS9m0KBBufnmm1vcROqVtt1223Tt2jV/+MMfWmx/X7NmTc4+++y89NJL6zxmyy23TEVFRUnfG0OHDs1uu+2WBx98MFOmTGkxN2XKlDzwwAPZddddc+CBB7b5mgDwVmT7MwB0cK+8mcW///3v/PWvfy2u+rr44otbnL33+c9/PnfddVduu+22vOMd78jw4cOzYsWK3HzzzfnnP/+ZL37xi8X/UV6yZEk+/OEPp7KyMjfccENxhdvYsWNz3333ZcqUKbn00kvzuc99rs29HnbYYRk6dGiOP/749OrVKw8++GAefPDB9OvXL9/4xjde9/GjRo3Kbbfdlptuuil77rlnjjrqqOJZanPmzMkJJ5ywzt1a3//+9+eGG27IEUcckb333jvV1dUZNmzY627ffv/735+bb745Rx99dIYPH54uXbpkxx13zEc+8pE2P99X2n333fPjH/84p556avbcc88ceuih2XXXXbNmzZo899xzeeCBB7LNNtvkiSeeeN1rfe5zn8utt96aW265JXvvvXc++MEPZsmSJbnpppsybNiw3H777S3qe/bsmRNPPDE33HBDBg8enEMOOST19fW555570rlz5wwePDizZs1q8ZjddtstvXv3zg033JDq6ursuOOOqaioyEc+8pHsuOOO+cxnPpOxY8dmr732yjHHHJPq6upMnz49f/3rX3PEEUfkjjvuaHG9T33qU5k3b16GDh2afv36paamJn/4wx/y61//OjvuuGNOPPHEYu1ll12Wp59+Ol/96ldzzTXX5MADD8x2222X+fPn5/HHH8/vf//7/PSnPy3eYfn1en0junXrlh//+Mc57rjjctBBB+W4445L375984c//CG/+tWv0rNnz+K5kOtLly5dctxxx+WKK67ID37wg/To0SMjRoxo02P/9Kc/5eijj86+++6bPfbYI9tvv31efPHF3HbbbVmzZk3xjMUkqa6uztlnn50LL7wwe+21Vz70oQ9l7dq1ueeee7L99ttn++23X+f6pX69SzFmzJg0NTVl3333bXXLe79+/XLKKaeksrIyn/rUp/KNb3wjgwYNypFHHpmGhob85je/yeLFi/Pe9763uIqwWbdu3fKud70rDzzwQEaPHp1dd901VVVVGTly5Kve/KaioiJXX311Dj744Jxwwgk58sgjs/vuu+fJJ5/Mrbfems033zw/+clPWtx5HQBoRQEA6JCSrPNWVVVV6NmzZ2HkyJGFX/3qV60+buXKlYWvf/3rhT333LPQuXPnQrdu3QpDhw4tXH/99S3qjj766EKSwqWXXrrONZYsWVLo379/obq6uvDwww+/bq/nn39+IUnhN7/5TeHKK68svOMd7yh07ty5sPXWWxdOOeWUwvz589d5zEEHHVRo7VeRxsbGwsSJEwvvfOc7C126dCl06dKlsPfeexcuu+yyQmNj4zr1L7zwQuHDH/5wYdttty1UVlYWkhTOP//81+157dq1hXPOOafQv3//QqdOnQpJCgcddFBxfscddyzsuOOOr/t8/9uf//znwsknn1zo27dvoaamprDlllsW9txzz8Lpp59euO+++163r2b19fWFz3zmM4Xtt9++UFtbW9htt90K//M//1N49tlnC0kKJ598cov6f//734WvfOUrhZ122qlQW1tb6NOnT+ETn/hE4aWXXnrV1/qRRx4pvO997yt07969UFFRsc5zav5adu3atdCjR4/CUUcdVfjzn//c6vO/8cYbCyeeeGJh5513Lmy22WaFzTffvLDnnnsWvvKVrxT++c9/rvO5V69eXfj+979fGDJkSKF79+6Fmpqawg477FB43/veV/jud79beOmll0rq9dWcfPLJhSSFOXPmtDr/yCOPFI466qjC1ltvXaiuri7ssMMOhbFjxxbmzZtX8rXa4oEHHij+PJ911lmvWvff34/PP/984ZxzzikccMABhe22265QU1NT6N27d+HQQw8tTJ06dZ3HNzU1FS655JLCgAEDis/rC1/4QuHf//73q35vl/L1bq3HQqH1n43W/i175dsrr7FmzZrCd77zncIee+xR6Ny5c2G77bYrjBkzpvD3v//9VV//p59+unD44YcXttpqq+L3xpVXXlkoFAqF3/zmN6/6b8ITTzxRGDNmTKFnz56FTp06FXr27FkYPXp04Yknnlin9rV+5ufMmdPqzyQAbOoqCoUS9zUBAPyXCRMm5IILLshvfvObvOc97yl3OwAAQDuzph8AAAAAKIlQEQAAAAAoiVARAAAAACiJMxUBAAAAgJJYqQgAAAAAlESoCAAAAACUpFO5G1hfmpqaMn/+/Gy++eapqKgodzsAAAAAsFEpFApZtmxZtt9++1RWvvZaxE0mVJw/f3522GGHcrcBAAAAABu1559/Pn369HnNmk0mVNx8882TvPyku3fvXuZuAAAAAGDjsnTp0uywww7FnO21bDKhYvOW5+7duwsVAQAAAOANasvRgm7UAgAAAACURKgIAAAAAJREqAgAAAAAlESoCAAAAACURKgIAAAAAJREqAgAAAAAlESoCAAAAACURKgIAAAAAJREqAgAAAAAlESoCAAAAACURKgIAAAAAJREqAgAAAAAlESoCAAAAACURKgIAAAAAJREqAgAAAAAlESoCAAAAACURKgIAAAAAJREqAgAAAAAlESoCAAAAACURKgIAAAAAJREqAgAAAAAlESoCAAAAACURKgIAAAAAJSkU7kbAACAjU1jY2Nmz56dRYsWpUePHhk0aFCqqqrK3RYAwAYjVAQAgBJMmzYtkyZNysKFC4tjPXv2zLhx4zJs2LAydgYAsOHY/gwAAG00bdq0TJgwIQMGDMjEiRMzderUTJw4MQMGDMiECRMybdq0crcIALBBVBQKhUK5m1gfli5dmrq6utTX16d79+7lbgcAgE1MY2NjxowZkwEDBuTCCy9MZeV//j7f1NSU8ePHZ86cObnmmmtshQYANkql5GtWKgIAQBvMnj07CxcuzOjRo1sEiklSWVmZUaNGZcGCBZk9e3aZOgQA2HCEigAA0AaLFi1KkvTv37/V+ebx5joAgE2ZUBEAANqgR48eSZI5c+a0Ot883lwHALApEyoCAEAbDBo0KD179sx1112XpqamFnNNTU25/vrr06tXrwwaNKhMHQIAbDhCRQAAaIOqqqqMGzcuM2bMyPjx4/PYY49lxYoVeeyxxzJ+/PjMmDEjY8eOdZMWAOAtwd2fAQCgBNOmTcukSZOycOHC4livXr0yduzYDBs2rIydAQC8OaXka0JFAAAoUWNjY2bPnp1FixalR48eGTRokBWKAMBGr5R8rdMG6gkAADYZVVVVGTx4cLnbAAAoG2cqAgAAAAAlESoCAAAAACURKgIAAAAAJREqAgAAAAAlESoCAAAAACURKgIAAAAAJREqAgAAAAAl6VTuBgAAYGPT2NiY2bNnZ9GiRenRo0cGDRqUqqqqcrcFALDBCBUBAKAE06ZNy6RJk7Jw4cLiWM+ePTNu3LgMGzasjJ0BAGw4tj8DAEAbTZs2LRMmTMiAAQMyceLETJ06NRMnTsyAAQMyYcKETJs2rdwtAgBsEBWFQqFQ7ibWh6VLl6auri719fXp3r17udsBAGAT09jYmDFjxmTAgAG58MILU1n5n7/PNzU1Zfz48ZkzZ06uueYaW6EBgI1SKfmalYoAANAGs2fPzsKFCzN69OgWgWKSVFZWZtSoUVmwYEFmz55dpg4BADYcoSIAALTBokWLkiT9+/dvdb55vLkOAGBTJlQEAIA26NGjR5Jkzpw5rc43jzfXAQBsyoSKAADQBoMGDUrPnj1z3XXXpampqcVcU1NTrr/++vTq1SuDBg0qU4cAABuOUBEAANqgqqoq48aNy4wZMzJ+/Pg89thjWbFiRR577LGMHz8+M2bMyNixY92kBQB4S3D3ZwAAKMG0adMyadKkLFy4sDjWq1evjB07NsOGDStjZwAAb04p+ZpQEQAAStTY2JjZs2dn0aJF6dGjRwYNGmSFIgCw0SslX+u0gXoCAIBNRlVVVQYPHlzuNgAAysaZigAAAABASYSKAAAAAEBJhIoAAAAAQEmEigAAAABASYSKAAAAAEBJhIoAAAAAQEmEigAAAABASTqVuwEAANjYNDY2Zvbs2Vm0aFF69OiRQYMGpaqqqtxtAQBsMEJFAAAowbRp0zJp0qQsXLiwONazZ8+MGzcuw4YNK2NnAAAbju3PAADQRtOmTcuECRMyYMCATJw4MVOnTs3EiRMzYMCATJgwIdOmTSt3iwAAG0RFoVAolLuJ9WHp0qWpq6tLfX19unfvXu52AADYxDQ2NmbMmDEZMGBALrzwwlRW/ufv801NTRk/fnzmzJmTa665xlZoAGCjVEq+ZqUiAAC0wezZs7Nw4cKMHj26RaCYJJWVlRk1alQWLFiQ2bNnl6lDAIANR6gIAABtsGjRoiRJ//79W51vHm+uAwDYlAkVAQCgDXr06JEkmTNnTqvzzePNdQAAmzKhIgAAtMGgQYPSs2fPXHfddWlqamox19TUlOuvvz69evXKoEGDytQhAMCG06ncDQAAwMagqqoq48aNy4QJE3Leeedlv/32S01NTRoaGvLII4/koYceyoQJE9ykBQB4SxAqAgBAGw0bNizHH398pkyZkhkzZhTHq6qqcvzxx2fYsGFl7A4AYMMRKgIAQBtNmzYtN910U/bff//st99+qa2tzerVq/PII4/kpptuysCBAwWLAMBbQkWhUCiUu4n1YenSpamrq0t9fX26d+9e7nYAANjENDY2ZsyYMRkwYEAuvPDCVFb+53jypqamjB8/PnPmzMk111xjCzQAsFEqJV9zoxYAAGiD2bNnZ+HChRk9enQKhUJmzZqV++67L7NmzUqhUMioUaOyYMGCzJ49u9ytAgC0O9ufAQCgDRYtWpQkmT9/fi688MIsXLiwONezZ8+ceuqpLeoAADZlQkUAAGiDHj16JEkuvvjiDBkyJOPHj0///v0zZ86cXHfddbnkkkta1AEAbMpsfwYAgDYYOHBgqqqqssUWW+SCCy7IwIED06VLlwwcODAXXHBBtthii1RVVWXgwIHlbhUAoN1ZqQgAAG3w17/+NY2NjfnXv/6V8ePHZ/vtt09DQ0Nqamoyf/78/Otf/yrWDR48uLzNAgC0M6EiAAC0QfNZifvvv38eeuihdebf9a535eGHH3amIgDwliBUBACANmg+K/Ghhx7KlltumYMPPjjbb7995s+fn3vuuScPP/xwizoAgE1ZyWcqLl++POeff34OPfTQbLXVVqmoqMhVV121Tt0pp5ySioqKdd523333dWqbmpryrW99K/3790/nzp3z9re/PT/96U/f0BMCAID2sNtuuyVJqqurc8MNN2TcuHE58sgjM27cuNxwww2prq5uUQcAsCkreaXiSy+9lK997Wvp27dv3vGOd+T+++9/1dra2tr86Ec/ajFWV1e3Tt25556bb3zjG/n4xz+efffdN7fddltGjRqVioqKnHjiiaW2CAAA690vfvGLJMmaNWtywQUXZNSoUcW7P19//fVZs2ZNse7YY48tZ6sAAO2u5FCxV69eWbBgQXr27JmZM2dm3333ffWLd+qUMWPGvOb15s2bl+985zs588wzc9lllyVJPvaxj+Wggw7KF77whRx33HGpqqoqtU0AAFiv5s2blyT5whe+kGuuuSZnnXVWca5Xr175/Oc/n//5n/8p1gEAbMpKDhVra2vTs2fPNtc3Njbm3//+d7p3797q/G233ZY1a9bkE5/4RHGsoqIi48aNy6hRozJjxowceOCBpbYJAADrVe/evZMkhUIh1157bWbPnp1FixalR48eGTRoUKZOndqiDgBgU1bymYqlWLFiRbp37566urpstdVWOfPMM7N8+fIWNY8++mg222yz7LHHHi3G99tvv+J8a1avXp2lS5e2eAMAgPYycuTIVFVV5YorrsjatWtbzK1duzZXXnllqqqqMnLkyDJ1CACw4bTb3Z979eqVL37xi9l7773T1NSUX/7yl/nBD36QP/3pT7n//vvTqdPLn3rBggXZbrvtUlFRsc7jk2T+/PmtXv+SSy7JBRdc0F7tAwBACzU1NTn22GNz4403Zvjw4WlqairOVVZWpqmpKSeccEJqamrK2CUAwIbRbqHiJZdc0uLjE088MbvuumvOPffcTJkypXgDlpUrV6a2tnadx3fu3Lk435pzzjknn/3sZ4sfL126NDvssMP6ah8AANYxcODAJGkRKL7y4+Z5AIBNXbtuf/5vn/nMZ1JZWZl77723ONalS5esXr16ndpVq1YV51tTW1ub7t27t3gDAID20tjYmEmTJuWAAw7IXXfdlTPPPDNHHXVUzjzzzNx111054IADMnny5DQ2Npa7VQCAdrdBQ8UuXbqkR48eWbx4cXGsV69eWbhwYQqFQovaBQsWJEm23377DdkiAAC0avbs2Vm4cGFGjx6dysqWv0ZXVlZm1KhRWbBgQWbPnl2mDgEANpx22/7cmmXLluWll17KNttsUxwbPHhwfvSjH+Xxxx9vsV3k4YcfLs4DAEC5LVq0KEly33335VOf+lSLFYmTJ0/OkUce2aIOAGBT1i4rFVetWpVly5atM37hhRemUCjk0EMPLY4deeSRqa6uzg9+8IPiWKFQyOTJk9O7d+8ccMAB7dEiAACUpEePHkmSn/3sZ+nevXs+//nP55ZbbsnnP//5dO/ePT/72c9a1AEAbMre0ErFyy67LEuWLCnemfmOO+7I3LlzkySf/OQn869//St77bVXPvzhD2f33XdPktx9992ZOnVqDj300OJfcZOkT58++fSnP51vf/vbWbNmTfbdd9/ceuuteeCBB3LdddelqqrqzT5HAAB403bbbbckSXV1dW644YbiXZ5HjBiRgw8+OIcffnjWrFlTrAMA2JRVFP77MMM26NevX/7xj3+0OjdnzpxsscUW+eQnP5mHHnoo8+fPT2NjY3beeeeMHj06n//851NdXd3iMU1NTfnmN7+Zyy+/PAsWLMguu+ySc845J6NHj25zT0uXLk1dXV3q6+vdtAUAgPVuypQpmThxYpLkXe96V2pqarJ8+fJ069YtDQ0NxeN7zjzzzBx77LHlbBUA4A0pJV97QysV//73v79uzTXXXNPm61VWVuacc87JOeec80baAQCAdjdv3rwkyU477VQMEF9pp512yrPPPlusAwDYlG3QG7UAAMDGqnfv3kmSZ599NtXV1Rk2bFh22223PPnkk5k2bVqeffbZFnUAAJsyoSIAALTBwQcfXNz+fOutt6Zr167FuRUrVmTEiBHFOgCATV273P0ZAAA2NVdddVXx/TFjxuSOO+7ISy+9lDvuuCNjxoxptQ4AYFNlpSIAALTB3LlzkyTDhw/P3XffnUsvvbQ4V1VVlcMOOyx33XVXsQ4AYFMmVAQAgDbo06dPZs6cmS222CJTp07N7bffnnnz5qV3794ZOXJkrrzyymIdAMCmzvZnAABog9NPPz1JcvPNN2flypWZN29e5s6dm3nz5mXlypW55ZZbWtQBAGzKrFQEAIA26NKlS4YOHZrp06fnqKOOKo7PnDkzt956a5Jk6NCh6dKlS3kaBADYgKxUBAAAAABKIlQEAIA2WLlyZaZPn57q6urceuutOeqoo7LPPvvkqKOOyq233prq6upMnz49K1euLHerAADtTqgIAABt8MMf/jBJctxxx6WmpqbFXE1NTY455pgWdQAAmzJnKgIAQBvMnTs3SfLEE09k+PDhxfHmMxX33nvvFnUAAJsyKxUBAKAN+vTpkyT54x//mOrq6owaNSrXXnttRo0alerq6vzxj39sUQcAsCmrKBQKhXI3sT4sXbo0dXV1qa+vT/fu3cvdDgAAm5jFixcXtzjfcccd6datW3Fu+fLlOeKII5Ikt9xyS7baaquy9AgA8GaUkq/Z/gwAAG1wzTXXFN//0Ic+lEGDBqVHjx5ZtGhRZs+e3aLu7LPPLkeLAAAbjFARAADaoPmsxG222SYvvvhiHn300RbzzePOVAQA3gqEigAA0AZ9+vTJzJkz8+KLL2aLLbZIv3790tTUlMrKyvz973/Piy++WKwDANjUCRUBAKANTjnllNx6661Jkuuuuy5du3Ytzq1YsSIjRowo1gEAbOqEigAA0Ab33HNP8f0jjzwyw4YNy2677ZYnn3wy06ZNa1F37LHHlqNFAIANRqgIAABtMG/evCTJTjvtlGeffTa//vWv8+tf/7o43zzeXAcAsCkTKgIAQBv07t07SfLss8/mXe96V2pra7Ns2bJsvvnmWb16dR5++OEWdQAAm7KKQqFQKHcT68PSpUtTV1eX+vr6dO/evdztAACwiVm5cmWGDx+e6urq3HnnnampqSnONTQ05PDDD8+aNWsyderUdOnSpYydAgC8MaXka1YqAgBAGzz55JNJkjVr1uT4449Pv379UigUUlFRkb///e9Zs2ZNsW7w4MFl7BQAoP0JFQEAoA0WLVqUJOnVq1cWLFiQP/3pTy3mm8eb6wAANmVCRQAAaIMePXokSRYsWJDq6uq87W1vS48ePbJo0aL85S9/yYIFC1rUAQBsyoSKAADQBjvvvHOSpKKiIrfffns6d+5cnFu1alWGDx+eQqFQrAMA2JQJFQEAoA2uuOKKJEmhUMjXvva17LfffqmpqUlDQ0MeeeSRNN//8IorrsjZZ59dzlYBANqdUBEAANpg7ty5SZLhw4fn7rvvzowZM4pzVVVVOeyww3LXXXcV6wAANmVCRQAAaIM+ffpk5syZmTp1aoYMGZL99tsvtbW1Wb16dR555JHcddddxToAgE1dZbkbAACAjcFpp52W5OUzFc8999z069cvNTU16devX84999xUVFS0qAMA2JRZqQgAAG3wzDPPJHn5TMXDDz/8NesGDx68gboCACgPKxUBAKANFi1atF7rAAA2ZkJFAABogy233DJJMmjQoNx555056qijss8+++Soo47KnXfembe97W0t6gAANmW2PwMAQBsUCoXi+126dMnZZ59d/Lipqal4puIr6wAANlVWKgIAQBssWbIkSTJ79uyMHz8+jz32WFasWJHHHnss48ePz+zZs1vUAQBsyqxUBACANujRo0eS5GMf+1juvPPOnHXWWcW5Xr165WMf+1h+9KMfFesAADZlQkUAAGiDQYMGpWfPnvnrX/+an/zkJ3nssceyaNGi9OjRI3vuuWcmTJiQXr16ZdCgQeVuFQCg3dn+DAAAbVBVVZVx48ZlxowZmTBhQqqrqzNkyJBUV1dnwoQJmTFjRsaOHZuqqqpytwoA0O4qCpvISdJLly5NXV1d6uvr071793K3AwDAJmratGmZNGlSFi5cWBzr1atXxo4dm2HDhpWxMwCAN6eUfE2oCAAAJWpsbMzs2bOL258HDRpkhSIAsNErJV9zpiIAAJSoqqoqgwcPLncbAABlI1QEAIASNTQ05Pbbb8+8efPSu3fvjBw5MjU1NeVuCwBggxEqAgBACSZPnpwpU6aksbGxxdixxx6bsWPHlrEzAIANR6gIAABtNHny5Nx4443Zcsstc9ppp2XIkCGZMWNGrrjiitx4441JIlgEAN4S3KgFAADaoKGhIcOHD0/37t1z0003pVOn//x9fu3atTn++OOzdOnSTJ061VZoAGCjVEq+VrmBegIAgI3a7bffnsbGxpx22mlpamrKlClT8r3vfS9TpkxJU1NTPvrRj6axsTG33357uVsFAGh3tj8DAEAbzJs3L0ny9NNP57vf/e46ZyqOGDGiRR0AwKZMqAgAAG3Qu3fvJMltt93W6pmKzSsUm+sAADZltj8DAEAbHHrooUmSioqKXHXVVVm5cmWuueaarFy5MldddVUqKipa1AEAbMqsVAQAgDb45S9/mSQpFAo58sgjW8xNnDixRd2xxx67QXsDANjQrFQEAIA2aOtZic5UBADeCqxUBACANthuu+2SJF27ds3NN9+cqVOnZt68eendu3eGDx+e4447LitWrCjWAQBsyoSKAABQosbGxsybNy9z584tfgwA8FYiVAQAgDZ44YUXkiQrVqzIyJEji+MzZ87Mrbfeuk4dAMCmzJmKAADQBr17916vdQAAGzOhIgAAtMHBBx9cfP/nP/95zjzzzBx11FE588wz8/Of/7zVOgCATZVQEQAA2uCqq64qvn/qqaemtrY2o0ePTm1tbU499dRW6wAANlXOVAQAgDZovinLiBEj8stf/jKXXnppca6qqiqHHXZY7rrrrmIdAMCmTKgIAABt0KdPn8ycOTN1dXWZOnVqbr/99sybNy+9e/fOyJEjc+WVVxbrAAA2dRWFQqFQ7ibWh6VLl6auri719fXp3r17udsBAGATs3LlygwfPjzV1dW58847U1NTU5xraGjI4YcfnjVr1mTq1Knp0qVLGTsFAHhjSsnXrFQEAIA26NKlS4YOHZrp06fnsMMOS//+/dOlS5esXLkyc+bMSVNTU4YOHSpQBADeEqxUBACAEhxzzDFZvHjxOuNbbbVVbrnlljJ0BACwfpSSr7n7MwAAtNF5552XxYsXp1OnTtlll12y5557ZpdddkmnTp2yePHinHfeeeVuEQBgg7D9GQAA2mDlypWZPn36a56pOH369KxcudIWaABgk2elIgAAtMEPf/jDJMlxxx2XqqqqzJo1K/fdd19mzZqVqqqqHHPMMS3qAAA2ZVYqAgBAG8ydOzdJsvXWW2fMmDFZuHBhca5nz5457rjjWtQBAGzKrFQEAIA26NOnT5Lk//7v/zJgwIBMnDgxU6dOzcSJEzNgwIB8//vfb1EHALApc/dnAABog+XLl+eII45IRUVFpk6dms6dOxfnVq1aleHDh6dQKOSOO+5It27dytgpAMAbU0q+ZvszAAC0wTPPPJMkKRQKOeKII9K/f//U1NSkoaEhc+bMSfPf6p955pkMHjy4jJ0CALQ/oSIAALTBokWLkiRbbLFFlixZkqeffrrFfPN4cx0AwKZMqAgAAG3Qo0ePJMmSJUtSWVmZnXbaKbW1tVm9enWeffbZLFmypEUdAMCmzI1aAACgDXbeeeckSUVFRX7+85/nkEMOyc4775xDDjkkP//5z1NRUdGiDgBgU2alIgAAtMEVV1yR5OUzFY888sgWcxMnTmxRd/bZZ2/Q3gAANjQrFQEAoA3mzp1bfL+ysuWv0a/8+JV1AACbKisVAQCgDXr16pUkqa2tzW233ZbHH388ixYtSo8ePbLHHnvkyCOPzOrVq4t1AACbMisVAQCgDXr27JkkWbt2bdasWZNnnnkmf/nLX/LMM89kzZo1Wbt2bYs6AIBNmZWKAADQBi+++GKSpLGxMUcccUSLuVeeqdhcBwCwKbNSEQAA2qB3797rtQ4AYGMmVAQAgDY49NBDkyQVFRX52c9+lqOOOir77LNPjjrqqPzsZz9LRUVFizoAgE2ZUBEAANrgl7/8ZZKkUCjk1FNPTXV1dYYOHZrq6uqceuqpKRQKLeoAADZlzlQEAIA2mDdvXpJkn332ycyZM3PzzTe3mG8eb64DANiUCRUBAKANms9KnDlzZvbff//07t07q1evTm1tbebNm5eHHnqoRR0AwKasotC8T2Mjt3Tp0tTV1aW+vj7du3cvdzsAAGxiVq5cmeHDh6e6ujp33nlnampqinMNDQ05/PDDs2bNmkydOjVdunQpY6cAAG9MKflayWcqLl++POeff34OPfTQbLXVVqmoqMhVV13VoqapqSlXXXVVRo4cmR122CGbbbZZ3va2t+Wiiy7KqlWr1rlmRUVFq2/f+MY3Sm0PAADaxZNPPpkkWbNmTU488cRccMEF+eY3v5kLLrggJ554YtasWdOiDgBgU1by9ueXXnopX/va19K3b9+84x3vyP33379OzYoVK/LRj340+++/f8aOHZttt902M2bMyPnnn5/77rsvv/71r4t3x2t28MEH56STTmoxttdee5XaHgAAtItFixYlSXbZZZc8/fTT6/we3DzeXAcAsCkrOVTs1atXFixYkJ49e2bmzJnZd99916mpqanJ9OnTc8ABBxTHPv7xj6dfv37FYPEDH/hAi8fsuuuuGTNmzBt4CgAA0P569OiRJHn66aezxRZbZPDgwencuXNWrVqVWbNm5emnn25RBwCwKSs5VKytrU3Pnj1fs6ampqZFoNjsQx/6UM4///w8/vjj64SKycvn1FRUVKRz586ltgUAAO1qt912S5JUV1fnxhtvfNUzFZvrAAA2ZRv07s8LFy5Mkmy99dbrzF111VX5wQ9+kEKhkD322CPnnXdeRo0a9arXWr16dVavXl38eOnSpeu/YQAA+P/94he/SPLymYpf/epXU1NTk+XLl6dbt25paGgonqn4i1/8Iscee2w5WwUAaHcbNFT81re+le7du+ewww5rMX7AAQfk+OOPT//+/TN//vxMnDgxo0ePTn19fcaNG9fqtS655JJccMEFG6JtAADIvHnzkiQ777xzHn744XXmd9pppzz77LPFOgCATdkGCxUvvvji3HvvvfnBD36QLbbYosXc9OnTW3x86qmn5p3vfGe+8pWv5JRTTkmXLl3Wud4555yTz372s8WPly5dmh122KFdegcAgN69eydJnnnmmVRXV2fYsGHZbbfd8uSTT2batGl59tlnW9QBAGzKNkioeOONN+a8887Laaed9qorD1+ppqYmZ511VsaOHZs//OEPOfDAA9epqa2tTW1tbXu0CwAA6zj44IMzceLEJMmtt96arl27FudWrFiRESNGFOsAADZ1le39Ce65556cdNJJGTFiRCZPntzmxzWvOly8eHF7tQYAAG121VVXFd8fM2ZM7rjjjrz00ku54447MmbMmFbrAAA2Ve26UvHhhx/Ohz70oeyzzz656aab0qlT2z/d3/72tyTJNtts017tAQBAm82dOzdJMnz48Nx999259NJLi3NVVVU57LDDctdddxXrAAA2Ze0WKj7++OMZMWJE+vXrlzvvvLPVcxGT5MUXX1wnOFy2bFn+93//N1tvvXXe+c53tleLAADQZn369MnMmTOzxRZbZOrUqbn99tszb9689O7dOyNHjsyVV15ZrAMA2NRVFAqFQqkPuuyyy7JkyZLMnz8/kyZNytFHH5299torSfLJT34ylZWV2XPPPTNv3rxcfPHF6xxWvdNOO2XIkCFJkgkTJuTWW2/NEUcckb59+2bBggX58Y9/nOeeey7XXHNNRo8e3aaeli5dmrq6utTX16d79+6lPiUAAHhNK1euzPDhw1NdXZ0777wzNTU1xbmGhoYcfvjhWbNmTaZOnfqqf1AHAOjISsnX3lCo2K9fv/zjH/9odW7OnDlJkv79+7/q408++eTiWTP33HNPvv3tb2f27NlZtGhRNttss+y333750pe+lPe9731t7kmoCABAezvvvPMyffr0VFdX55hjjsnw4cMzderU3HLLLVmzZk2GDh2aiy66qNxtAgC8Ie0eKnZEQkUAADaE5mDxvwkUAYCNnVBRqAgAQDtauXJlfvjDH2bu3Lnp06dPTj/9dFueAYCNXin5Wrve/RkAADqCVatW5bnnnluv1zzssMOK7z///PPr9dp9+/ZN586d1+s1AQDWJ6EiAACbvOeeey5nnHFGudtos8svvzy77rprudsAAHhVQkUAADZ5ffv2zeWXX75er/ncc8/l61//es4999z07dt3vV57fV8PAGB9EyoCALDJ69y5c7ut/Ovbt69VhQDAW05luRsAAAAAADYuQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkJYeKy5cvz/nnn59DDz00W221VSoqKnLVVVe1Wvv444/n0EMPTbdu3bLVVlvlIx/5SF588cV16pqamvKtb30r/fv3T+fOnfP2t789P/3pT0t+MgAAAABA+ys5VHzppZfyta99LY8//nje8Y53vGrd3LlzM2zYsDzzzDO5+OKL8/nPfz6/+MUvcvDBB6ehoaFF7bnnnpsvfelLOfjgg/P9738/ffv2zahRo3LDDTeU/owAAAAAgHbVqdQH9OrVKwsWLEjPnj0zc+bM7Lvvvq3WXXzxxfn3v/+dP/zhD+nbt2+SZL/99svBBx+cq666KqeffnqSZN68efnOd76TM888M5dddlmS5GMf+1gOOuigfOELX8hxxx2XqqqqN/r8AAAAAID1rOSVirW1tenZs+fr1t1yyy05/PDDi4FiknzgAx/Irrvumptuuqk4dtttt2XNmjX5xCc+URyrqKjIuHHjMnfu3MyYMaPUFgEAAACAdtQuN2qZN29e/vnPf2afffZZZ26//fbLo48+Wvz40UcfzWabbZY99thjnbrm+dasXr06S5cubfEGAAAAALS/dgkVFyxYkOTlrdL/rVevXlm8eHFWr15drN1uu+1SUVGxTl2SzJ8/v9XPcckll6Surq74tsMOO6zPpwAAAAAAvIp2CRVXrlyZ5OWt0v+tc+fOLWpWrlzZprr/ds4556S+vr749vzzz6+X3gEAAACA11byjVraokuXLklSXI34SqtWrWpR06VLlzbV/bfa2tpWw0gAAAAAoH21y0rF5q3LzdugX2nBggXZaqutioFgr169snDhwhQKhXXqkmT77bdvjxYBAAAAgDeoXULF3r17Z5tttsnMmTPXmXvkkUcyePDg4seDBw/OihUr8vjjj7eoe/jhh4vzAAAAAEDH0S6hYpIcc8wxufPOO1ucdXjfffflqaeeynHHHVccO/LII1NdXZ0f/OAHxbFCoZDJkyend+/eOeCAA9qrRQAAAADgDXhDZypedtllWbJkSfHOzHfccUfmzp2bJPnkJz+Zurq6fOUrX8nNN9+c9773vTn77LOzfPnyfPvb386gQYPy0Y9+tHitPn365NOf/nS+/e1vZ82aNdl3331z66235oEHHsh1112Xqqqq9fA0AQAAAID1paLw34cZtkG/fv3yj3/8o9W5OXPmpF+/fkmSxx57LJ/97Gfz4IMPpqamJiNGjMh3vvOdbLfddi0e09TUlG9+85u5/PLLs2DBguyyyy4555xzMnr06Db3tHTp0tTV1aW+vj7du3cv9SkBAEBJnnrqqZxxxhm5/PLLs+uuu5a7HQCAN62UfO0NrVT8+9//3qa6PffcM3fffffr1lVWVuacc87JOeec80baAQAAAAA2oHY7UxEAAAAA2DQJFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICSCBUBAAAAgJIIFQEAAACAkggVAQAAAICStFuoeMopp6SiouJV3+bNm5ckec973tPq/KGHHtperQEAAAAAb0Kn9rrwGWeckQ984AMtxgqFQsaOHZt+/fqld+/exfE+ffrkkksuaVG7/fbbt1drAAAAAMCb0G6h4pAhQzJkyJAWYw8++GBWrFiR0aNHtxivq6vLmDFj2qsVAAAAAGA92qBnKl5//fWpqKjIqFGj1plbu3Ztli9fviHbAQAAAADegA0WKq5ZsyY33XRTDjjggPTr16/F3FNPPZXNNtssm2++eXr27Jnx48dnzZo1G6o1AAAAAKAE7bb9+b/dfffdWbRo0Tpbn3faaae8973vzaBBg/Lvf/87U6ZMyUUXXZSnnnoqN95446teb/Xq1Vm9enXx46VLl7Zb7wAAAADAf2ywUPH6669PdXV1jj/++BbjV1xxRYuPP/KRj+T000/P//t//y+f+cxnsv/++7d6vUsuuSQXXHBBu/ULAAAAALRug2x/Xr58eW677bZ88IMfTI8ePV63/nOf+1yS5N57733VmnPOOSf19fXFt+eff3699QsAAAAAvLoNslLx1ltvbfWuz69mhx12SJIsXrz4VWtqa2tTW1u7XvoDAAAAANpug6xUvO6669KtW7eMHDmyTfV/+9vfkiTbbLNNe7YFAAAAALwB7R4qvvjii7n33nvzoQ99KF27dm0xt3Tp0hY3W0mSQqGQiy66KEnywQ9+sL3bAwAAAABK1O7bn2+88casXbu21a3Pf/zjH/PhD384H/7wh7Pzzjtn5cqV+fnPf57p06fn9NNPz957793e7QEAAAAAJWr3UPG6667Ltttumw984APrzO24445597vfnZ///OdZuHBhKisrs8cee2Ty5Mk5/fTT27s1AIAOrbGxMbNnz86iRYvSo0ePDBo0KFVVVeVuCwAA2j9UnDFjxqvO9e/fPzfddFN7twAAsNGZNm1aJk2alIULFxbHevbsmXHjxmXYsGFl7AwAADbQjVoAAGi7adOmZcKECRkwYEAmTpyYqVOnZuLEiRkwYEAmTJiQadOmlbtFAADe4oSKAAAdSGNjYyZNmpQhQ4bkwgsvzMCBA9OlS5cMHDgwF154YYYMGZLJkyensbGx3K0CAPAWJlQEAOhAZs+enYULF2b06NGprGz5q1plZWVGjRqVBQsWZPbs2WXqEAAAhIoAAB3KokWLkrx89nRrmseb6wAAoByEigAAHUiPHj2SJHPmzGl1vnm8uQ4AAMpBqAgA0IEMGjQoPXv2zHXXXZempqYWc01NTbn++uvTq1evDBo0qEwdAgCAUBEAoEOpqqrKuHHjMmPGjIwfPz6PPfZYVqxYkcceeyzjx4/PjBkzMnbs2FRVVZW7VQAA3sI6lbsBAABaGjZsWCZMmJBJkyblrLPOKo736tUrEyZMyLBhw8rYHQAACBUBADqkYcOGZejQoZk9e3YWLVqUHj16ZNCgQVYoAgDQIQgVAQA6qKqqqgwePLjcbQAAwDqcqQgAAAAAlESoCAAAAACURKgIAAAAAJTEmYoAAB1UQ0NDbr/99sybNy+9e/fOyJEjU1NTU+62AABAqAgA0BFNnjw5U6ZMSWNjY4uxY489NmPHji1jZwAAIFQEAOhwJk+enBtvvDFbbrllTjvttAwZMiQzZszIFVdckRtvvDFJBIsAAJSVMxUBADqQhoaGTJkyJVtuuWVuuummjBgxIltttVVGjBiRm266KVtuuWWmTJmShoaGcrcKAMBbmFARAKADuf3229PY2JjTTjstnTq13FTSqVOnfPSjH01jY2Nuv/32MnUIAABCRQCADmXevHlJkiFDhrQ63zzeXAcAAOUgVAQA6EB69+6dJJkxY0ar883jzXUAAFAOQkUAgA5k5MiRqaqqyhVXXJG1a9e2mFu7dm2uvPLKVFVVZeTIkWXqEAAA3P0ZAKBDqampybHHHpsbb7wxxx13XA4++OBsv/32mT9/fu65554sWbIkJ5xwQmpqasrdKgAAb2FCRQCADmbs2LGZO3dupk+fnptvvrnF3NChQzN27NgydQYAAC8TKgIAdDDTpk3L7373u+y///7p3bt3Vq9endra2sybNy+/+93vMm3atAwbNqzcbQIA8BYmVAQA6EAaGxszadKkDBkyJBdeeGEqK/9zBHZTU1PGjx+fyZMnZ+jQoamqqipjpwAAvJW5UQsAQAcye/bsLFy4MKNHj24RKCZJZWVlRo0alQULFmT27Nll6hAAAISKAAAdyqJFi5Ik/fv3b3W+eby5DgAAykGoCADQgfTo0SNJMmfOnFbnm8eb6wAAoByEigAAHcigQYPSs2fPXHfddWlqamox19TUlOuvvz69evXKoEGDytQhAAAIFQEAOpSqqqqMGzcuM2bMyPjx4/PYY49lxYoVeeyxxzJ+/PjMmDEjY8eOdZMWAADKyt2fAQA6mGHDhmXChAmZNGlSzjrrrOJ4r169MmHChAwbNqyM3QEAgFARAKBDGjZsWIYOHZrZs2dn0aJF6dGjRwYNGmSFIgAAHYJQEQCgg6qqqsrgwYPL3QYAAKzDmYoAAAAAQEmEigAAAABASYSKAAAAAEBJhIoAAAAAQEmEigAAAABASYSKAAAAAEBJhIoAAAAAQEk6lbsBAABozQsvvJD6+vpyt/GqnnvuuRb/7ajq6uqy3XbblbsNAGATU1EoFArlbmJ9WLp0aerq6lJfX5/u3buXux0AAN6EF154ISedfHIaVq8udysbvZra2vzk6qsFiwDA6yolX7NSEQCADqe+vj4Nq1en6sB3paLOH4zfqEL90jQ8+HDq6+uFigDAeiVUBACgw6qo656KHluVuw0AAP6LG7UAAAAAACURKgIAAAAAJREqAgAAAAAlESoCAAAAACURKgIAAAAAJREqAgAAAAAlESoCAAAAACURKgIAAAAAJREqAgAAAAAlESoCAAAAACURKgIAAAAAJelU7gYAAGhdY2NjZs+enUWLFqVHjx4ZNGhQqqqqyt0WAAAIFQEAOqJp06Zl0qRJWbhwYXGsZ8+eGTduXIYNG1bGzgAAwPZnAIAOZ9q0aZkwYUIGDBiQiRMnZurUqZk4cWIGDBiQCRMmZNq0aeVuEQCAtzihIgBAB9LY2JhJkyZlyJAhufDCCzNw4MB06dIlAwcOzIUXXpghQ4Zk8uTJaWxsLHerAAC8hQkVAQA6kNmzZ2fhwoUZPXp0Kitb/qpWWVmZUaNGZcGCBZk9e3aZOgQAAKEiAECHsmjRoiRJ//79W51vHm+uAwCAchAqAgB0ID169EiSzJkzp9X55vHmOgAAKAehIgBABzJo0KD07Nkz1113XZqamlrMNTU15frrr0+vXr0yaNCgMnUIAABJp3I3AADAf1RVVWXcuHGZMGFCzj333Gy//fZpaGhITU1N5s+fn4cffjgTJkxIVVVVuVsFAOAtTKgIANDBDBs2LAcccECmT5++ztzQoUMzbNiwMnQFAAD/IVQEAOhgJk+enOnTp2fLLbfMwQcfnO233z7z58/PPffck+nTp2fy5MkZO3ZsudsEAOAtTKgIANCBNDQ0ZMqUKdlyyy1z0003pVOn//y69vGPfzzHH398pkyZklNPPTU1NTVl7BQAgLcyN2oBAOhAbr/99jQ2Nua0005LRUVFZs2alfvuuy+zZs1KRUVFPvrRj6axsTG33357uVsFAOAtzEpFAIAOZN68ecX3x4wZk4ULFxY/7tmzZ0aPHr1OHQAAbGhCRQCADqR3795Jkv/5n//JAQcckPHjx6d///6ZM2dOrrvuunznO99pUQcAAOVg+zMAQAcyYsSIJEl1dXXOP//8DBw4MF26dMnAgQNz/vnnp7q6ukUdAACUg5WKAAAdyJNPPpkkWbNmTU488cQcfPDB6dWrVxYsWJB77rkna9asKdYNHjy4jJ0CAPBWJlQEAOhAFi1alCTZf//989BDD+Wmm25qMf+ud70rDz/8cLEOAADKQagIANCB9OjRI0ny8MMPZ//990/v3r2zevXq1NbWZt68eXn44Ydb1AEAQDkIFQEAOpCBAwemqqoq3bt3z4UXXphOnf7z69ratWtz/PHHZ+nSpRk4cGAZuwQA4K1OqAgA0IH89a9/TWNjY/71r3/lvPPOS21tbZYtW5bNN988q1evzr/+9a9inTMVAQAoF6EiAEAH0nxW4i677FLc6vxKu+yyS55++mlnKgIAUFZCRQCADqT5rMSnn3461dXVGTZsWHbbbbc8+eSTmTZtWp5++ukWdQAAUA6V5W4AAID/2HnnnZMkFRUVuf7667Nq1arcddddWbVqVa6//vpUVFS0qAMAgHKwUhEAoAO54oorkiSFQiHHHXdccXzOnDmZPn16i7qzzz57g/cHAACJlYoAAB3K3Llz12sdAAC0BysVAQA6kG233bb4/p133lm8KUuPHj2yyy675PDDD1+nDgAANrR2W6l4//33p6KiotW3hx56qEXt7373uxx44IHp2rVrevbsmU996lNZvnx5e7UGANBhLV68OMnLZyoWCoU888wz+ctf/pJnnnkmhUKheKZicx0AAJRDu69U/NSnPpV99923xdgrDxafNWtW3v/+92ePPfbIpZdemrlz5+Z//ud/8vTTT+euu+5q7/YAADqUF154IcnLZyoeccQRLeYmTpy4Th0AAJRDu4eK7373u3Pssce+6vxXvvKVbLnllrn//vvTvXv3JEm/fv3y8Y9/PL/61a9yyCGHtHeLAAAdxvbbb585c+a0qQ4AAMplg9yoZdmyZVm7du0640uXLs0999yTMWPGFAPFJDnppJPSrVu33HTTTRuiPQCADuOzn/1s8f2bbropRx11VPbZZ58cddRRLX43emUdAABsaO0eKn70ox9N9+7d07lz57z3ve/NzJkzi3OzZ8/O2rVrs88++7R4TE1NTQYPHpxHH330Va+7evXqLF26tMUbAMDG7te//nXx/RNOOCHLly/Pxz/+8SxfvjwnnHBCq3UAALChtVuoWFNTk2OOOSbf+973ctttt+Wiiy7K7Nmz8+53v7sYFi5YsCBJ0qtXr3Ue36tXr8yfP/9Vr3/JJZekrq6u+LbDDju0zxMBANiA5s2blyTZaaedUigUcu+99+aMM87Ivffem0KhkJ122qlFHQAAlEO7hYoHHHBApkyZklNPPTUjR47Ml7/85Tz00EOpqKjIOeeckyRZuXJlkqS2tnadx3fu3Lk435pzzjkn9fX1xbfnn3++fZ4IAMAG1Lt37yTJhz70odxxxx0ZOnRo+vfvn6FDh+aOO+7IkUce2aIOAADKod1v1PJKO++8c4488sj87Gc/S2NjY7p06ZLk5a3M/23VqlXF+dbU1ta2GkYCAGzMRo4cmcmTJ+eKK67IBz/4wVx00UXFubVr1+bKK69MVVVVRo4cWcYuAQB4q9ugoWKS7LDDDmloaMi///3v4rbn5m3Qr7RgwQJ3NQQA3nJqampy7LHH5sYbb8zRRx+dbbfdNg0NDampqck///nPLFu2LCeccEJqamrK3SoAAG9hG+Tuz6/0t7/9LZ07d063bt3ytre9LZ06dWpx85YkaWhoyKxZszJ48OAN3R4AQNmNHTs2W221VZYtW5Znn302zz//fJ599tksW7YsW221VcaOHVvuFgEAeItrt1DxxRdfXGfsT3/6U26//fYccsghqaysTF1dXT7wgQ/k2muvzbJly4p111xzTZYvX57jjjuuvdoDAOiwxo0bl8WLF6eioiK77rpr3vOe92TXXXdNRUVFFi9enHHjxpW7RQAA3uLabfvzCSeckC5duuSAAw7Itttum7/+9a/54Q9/mK5du+Yb3/hGse7rX/96DjjggBx00EE5/fTTM3fu3HznO9/JIYcckkMPPbS92gMA6JCWL1+eJ554IhUVFZk6dWo6d+5cnFu1alWGDx+eJ554IsuXL0+3bt3K2CkAAG9l7bZS8aijjspLL72USy+9NJ/4xCeK5wLNnDkze+yxR7Fu7733zr333psuXbrkM5/5TH74wx/mtNNOy5QpU9qrNQCADqv5j68HH3xwqqurM2vWrNx3332ZNWtWqqur8/73v79FHQAAlEO7rVT81Kc+lU996lNtqj3wwAMzffr09moFAGCjMX/+/CTJgAEDMmbMmCxcuLA417Nnzxx55JG59957i3UAAFAOG/xGLQAAvLrtt98+STJ58uQMGDAgEydOzNSpUzNx4sQMGDAgl19+eYs6AAAoB6EiAEAH8oUvfKH4/pe//OU0NDTkd7/7XRoaGvLlL3+51ToAANjQ2m37MwAApZszZ07x/ZEjR75m3eDBgzdARwAAsC4rFQEAOpBFixat1zoAAGgPQkUAgA5kyy23TJIMGjQot912W4YOHZr+/ftn6NChue222/K2t72tRR0AAJSD7c8AAB1IoVAovl9VVZVtttkmq1evzjbbbJOqqqpUVFSsUwcAABuaUBEAoANZsmRJkmT27Nk5/PDDi+MzZ87Mrbfeuk4dAACUg+3PAAAdSI8ePdZrHQAAtAcrFQEAOpCdd945SVJRUZE77rgjTz/9dBYtWpQePXpkl112yRFHHJFCoVCsAwCAcrBSEQCgA7niiiuSvHxm4sUXX5zq6uoMGTIk1dXVufjii4tnKTbXAQBAOVipCADQgcydOzdJ8qlPfSo33XRTzjrrrOJcr1698slPfjLf//73i3UAAFAOQkUAgA6kT58+mTlzZl566aVce+21mT17dnH786BBg/KjH/2oWAcAAOVi+zMAQAdy+umnJ0luvvnmNDY2ZvDgwXn/+9+fwYMHp7GxMbfcckuLOgAAKAcrFQEAOpAuXbpk6NChmT59eoYPH57+/func+fOWbVqVebMmZPGxsYMHTo0Xbp0KXerAAC8hQkVAQA6mIsuuijHHHNMFi9enGeeeabF3FZbbZWLLrqoTJ0BAMDLbH8GAOhgzjvvvCxevDidOnXKLrvskj333DO77LJLOnXqlMWLF+e8884rd4sAALzFWakIANCBrFy5MtOnT091dXXuvPPO1NTUFOcaGhpy+OGHZ/r06Vm5cqUt0AAAlI2VigAAHcgPf/jDJMlxxx3XIlBMkpqamhxzzDEt6gAAoByEigAAHcjcuXOTJMOHD291vnm8uQ4AAMpBqAgA0IH06dMnSTJ16tRW55vHm+sAAKAchIoAAB3I6aefniS5+eab09DQ0GKuoaEht9xyS4s6AAAoBzdqAQBYT1atWpXnnnvuTV/n7W9/e/785z9n+PDhef/7358DDzwwDz74YO677740Njbm7W9/e55//vk39Tn69u2bzp07v+leAQB4axIqAgCsJ88991zOOOOM9Xa9xsbG/OpXv8qvfvWrFuN//vOf3/Tnufzyy7Prrru+qWsAAPDWJVQEAFhP+vbtm8svv3y9Xa+hoSFXXnll/vjHP2bvvffORz/60XXuCP1G9e3bd71cBwCAtyahIgDAetK5c+f1vvrvjDPOKL5ZWQgAQEfhRi0AAAAAQEmEigAAAABASYSKAAAAAEBJhIoAAAAAQEmEigAAAABASYSKAAAAAEBJhIoAAAAAQEmEigAAAABASYSKAAAAAEBJhIoAAAAAQEmEigAAAABASTqVuwEAAHg1hfql5W5ho+b1AwDai1ARAIAOq/HBh8vdAgAArRAqAgDQYVUd+K5U1HUvdxsbrUL9UsEsANAuhIoAAHRYFXXdU9Fjq3K3AQDAf3GjFgAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAk7RYq/v73v89ZZ52VPffcM5tttln69u2b448/Pk899VSLulNOOSUVFRXrvO2+++7t1RoAAAAA8CZ0aq8Lf/Ob38z06dNz3HHH5e1vf3sWLlyYyy67LHvvvXceeuihvO1tbyvW1tbW5kc/+lGLx9fV1bVXawAAAADAm9BuoeJnP/vZXH/99ampqSmOnXDCCRk0aFC+8Y1v5Nprr/1PE506ZcyYMe3VCgAAAACwHrXb9ucDDjigRaCYJLvsskv23HPPPP744+vUNzY2ZunSpe3VDgAAAACwnmzQG7UUCoW88MIL2XrrrVuMr1ixIt27d09dXV222mqrnHnmmVm+fPmGbA0AAAAAaKN22/7cmuuuuy7z5s3L1772teJYr1698sUvfjF77713mpqa8stf/jI/+MEP8qc//Sn3339/OnVqvcXVq1dn9erVxY+tcgQAAACADWODhYpPPPFEzjzzzAwZMiQnn3xycfySSy5pUXfiiSdm1113zbnnnpspU6bkxBNPbPV6l1xySS644IJ27RkAAAAAWNcG2f68cOHCjBgxInV1dZkyZUqqqqpes/4zn/lMKisrc++9975qzTnnnJP6+vri2/PPP7++2wYAAAAAWtHuKxXr6+tz2GGHZcmSJXnggQey/fbbv+5junTpkh49emTx4sWvWlNbW5va2tr12SoAAAAA0AbtGiquWrUqRxxxRJ566qnce++9GThwYJset2zZsrz00kvZZptt2rM9AAAAAOANaLdQsbGxMSeccEJmzJiR2267LUOGDFmnZtWqVVmzZk0233zzFuMXXnhhCoVCDj300PZqDwAAAAB4g9otVPzc5z6X22+/PUcccUQWL16ca6+9tsX8mDFjsnDhwuy111758Ic/nN133z1Jcvfdd2fq1Kk59NBDc+SRR7ZXewAAAADAG9RuoeKsWbOSJHfccUfuuOOOdebHjBmTLbbYIocffnjuueeeXH311WlsbMzOO++ciy++OJ///OdTWblB7iMDAAAAAJSg3ULF+++//3Vrtthii1xzzTXt1QIAAAAA0A4sBQQAAAAASiJUBAAAAABK0m7bnwEA4M0q1C8tdwsbNa8fANBehIoAAHQ4dXV1qamtTcODD5e7lY1eTW1t6urqyt0GALCJESoCANDhbLfddvnJ1Venvr6+3K28queeey5f//rXc+6556Zv377lbudV1dXVZbvttit3GwDAJkaoCABAh7TddtttFGFY3759s+uuu5a7DQCADUqoCAC8Zb3wwgsdeiVc8vJquFf+t6OyGg4A4K1FqAgAvCW98MILOenkk9KwuqHcrbTJ17/+9XK38Jpqamvyk6t/IlgEAHiLECoCAG9J9fX1aVjdkIp3756KLbqWu52NWmHJijQ88ETq6+uFigAAbxFCRQDgLa1ii66p6LF5udvY6BXK3QAAABtUZbkbAAAAAAA2LkJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkQkUAAAAAoCRCRQAAAACgJEJFAAAAAKAkncrdAOtqbGzM7Nmzs2jRovTo0SODBg1KVVVVudsCAAAAgCRCxQ5n2rRpmTRpUhYuXFgc69mzZ8aNG5dhw4aVsTMAAAAAeJlQsQOZNm1aJkyYkH333Tfbbrtt6uvrU1dXl86dO2fChAmZMGGCYBHYKK1cuTI//OEPM3fu3PTp0yenn356unTpUu62AAAAeIOEih1EY2NjJk2alM022yyPPPLIOvPdunXL5MmTM3ToUFuhgY3Keeedl+nTpxc/njlzZm699dYMHTo0F110URk7AwAA4I1yo5YOYvbs2Vm4cGGWL1+eioqK7LrrrnnPe96TXXfdNRUVFVm+fHkWLFiQ2bNnl7tVgDZrDhSrq6szatSoXHvttRk1alSqq6szffr0nHfeeeVuEQAAOrTGxsbMmjUr9913X2bNmpXGxsZytwRJrFTsMObNm1d8v1Ao5KmnnspTTz3Vat3gwYM3YGcAb8zKlSuLgeKdd96ZmpqaJMnHP/7xnHzyyTn88MMzffr0rFy50lZoAABohfsu0JFZqdhB/PKXvyy+v8UWW+T444/Ppz/96Rx//PHZYostWq0D6Mh++MMfJkmOO+64YqDYrKamJsccc0yLOgAA4D+a77swYMCATJw4MVOnTs3EiRMzYMCATJgwIdOmTSt3i7zFWanYQSxbtixJUllZmdra2tx0003Fue222y6VlZVpamoq1gF0dHPnzk2SDB8+vNX54cOH54YbbijWAQAAL2u+78KQIUNy4YUXprLy5TVhAwcOzIUXXpjx48e77wJlZ6ViB1FRUZEkaWpqSkNDQ97znvfk0EMPzXve8540NDSkqampRR1AR9enT58kydSpU7Ny5cp873vfyxe+8IV873vfy8qVKzN16tQWdQAAwMua77swevToLFq0KCeeeGIOO+ywnHjiiVm0aFFGjRrlvguUnZWKHcSee+6Zv//970mSf/3rX7n//vtftQ5gY3D66afn1ltvzU9/+tNcf/31xfHmuz83/5Hk9NNPL1eLAADQIS1atChJ8oUvfCErVqwojq9atSrHH398unbt2qIOysFKxQ6iurp6vdYBlFuXLl2y1VZbpVAoJEn69u2bUaNGpW/fvklevinVVltt5SYtAADwX3r06JEkLQLFV2oeb66DchAqdhA777zzeq0DKLeVK1dm8eLFxY+fe+65XH/99XnuueeKY4sXL87KlSvL0R4AAHRYvXr1avHxfvvtl8suuyz77bffa9bBhmT7cwfx5JNPtrluxIgR7dwNwJv3yrs677ffflm1alXq6+tTV1eXzp0755FHHinWnX322eVqE1JY0voKANrOawgA69dZZ51VfH/ffffNSSedlP79++ekk05KoVDI73//+2LdzTffXK42eYsTKnYQpYSKABuD559/PknSv3///OMf/8gLL7xQnNtuu+3Sv3//zJkzp1gH5VJ44IkUyt0EAMArNJ+V2LNnzzz//PMtQsZevXplu+22ywsvvOBMRcpKqNhBzJkzZ73WAZRb586dk7z879YBBxyQr371q8Ug8brrrsvvfve7FnVQLhXv3j0VW3QtdxsbtcKSFSk88ES52wCADmPVqlUtjv15o9asWZOLLrooTz/9dHHXzy677JKvfOUrxZqnnnrqTX2Ovn37+p2cN0So2EGsWbNmvdYBlNuQIUMyffr0VFRUZPz48cVfVAYOHJjx48dn+PDhKRQKGTJkSJk75a2uYouuqeixebnb2OhZ7QkA//Hcc8/ljDPOeNPXWbRoUcaNG/eq84VC4U1/nssvvzy77rrrm7oGb01CxQ6qb9++OfDAA/Pggw+ul79uAGxozTdgKRQKGTlyZI455pgMHz48U6dOzS233FK8K7QbtQAAsKnp27dvLr/88jf8+BUrVuQzn/lM8eMePXpk0aJFxf82++53v5uuXd/cjou+ffu+qcfz1iVU7KCa75IKsLGqq6tLkmyxxRZZsmRJbrjhhtxwww3F+ebx5joAANhUdO7c+U2v/uvdu3fmzZuX5D9nLL4yUOzdu3cGDx78pj4HvBlCxfVgfZ2V0FbOSwA2Bttss02SpL6+Pu9617tSU1OT5cuXp1u3bmloaCje/bm5DgAA+I9rr702Y8aMKQaLr9S7d+9ce+21ZegK/kOouB6sr7MS2sp5CcDGYNCgQenZs2fq6ury97//vcXdn3v27Jldd901S5cuzaBBg8rYJQAAdFzXXntt6uvr87nPfS7PPvtsdtppp3znO9+x24cOQai4HrzZsxKS5Fvf+laeffbZ163baaed8sUvfvFNfS7nJQAbQlVVVcaNG5fzzz9/nbmFCxdm4cKFueCCC1JVVVWG7gAAYONQV1eXL37xiznjjDPyxS9+UaBIhyFUXA/Wx1kJ//u//5sjjjiiTXXdunV7U58LYEP561//+rrzw4YN20DdAAAAsL4IFTuIbt26Zffdd88TTzzxqjW77767QBHYaDQ0NOTmm29OkrzrXe/K/vvvn9ra2qxevToPPfRQHn744dx888059dRTU1NTU+ZuAQAAKIVQsQOZNGlSxo0b12qwuPvuu2fSpEll6Argjfn5z3+epqam7LTTTrn44otTWVlZnBs5cmQ+/vGP529/+1t+/vOf54QTTihjpwAAvNW98MILqa+vL3cbr6r55rAb8iaxb1RdXV222267crfBBiBU7GAmTZqU5cuX57zzzsuf/vSnvOMd78hFF11khSKw0fnLX/6SJDnttNNaBIpJUllZmVNPPTXnnXde/vKXvwgVAQAomxdeeCEnn3RyVjesLncrr+vrX/96uVt4XbU1tbn6J1cLFt8ChIodULdu3fKJT3wiZ5xxRj7xiU8IFIGNUufOnZO8fFOW1jSPN9cBAEA51NfXZ3XD6py689j06rJ9udvZqC1YOT8/fmZy6uvrhYpvAZWvXwIApfvgBz+YJLnyyiuzdu3aFnNr167N1Vdf3aIOAACAjYeVigC0i7322itdu3bNsmXLcvTRR2fbbbdNQ0NDampq8s9//jPLli1L165ds9dee5W7VQAAyI+fmVzuFmCjIlQEoF1UVVXlS1/6Us4///wsW7Ysy5YtW6fmS1/6UqqqqsrQHQAAtGT785vXvP2ZtwbbnwFoNz/96U/f1DwAAAAd01t2paLbxa8fbhUPvJrly5fniSeeSJJUVFSkUCgU55o/fuKJJ7J8+XI3pAIAoGzq6upSW1Nrhd168v+xd9dxUeT/H8Dfw1LS0g2KYKPYBSoKttiJ3mG3nnF2d/dZnAp249nd3YVioKLYKIggtbx+f/Db+e4AenoCu8D7+Xjc42Bmdv3sDjPzmdd8Qkdbh4yNjVVdDJYD8mWomDZdfGdKTEpSdVH+lbpPF6+jrU1BwcEcLDLGMpg2bZr4s3KgmP73adOmSbZljDHGGGMsJ1lZWVFQcJDaNzyaOnUqjR49mhwdHVVdnO/Kb42P7t27R/369RN/X7JkCZUsWVKFJco5+TJUTJsuPon6FK9IdvqGqi5OrhUZF0t/3b/CU8UzxjL19OnTLN2OMcYYY4yx7GJlZZUr7msdHR3Jzc1N1cVg/6927doZlikCxhMnTuR0cXJcvgwVFez0DamQYUFVF4MxxvIkDY0fG7b3R7djjDHGGGMst0hISMjS4cyyc4g0R0dH0tXVzfL3zevSB4qNGjWiffv2Sdbn9WAxX4eKjDHGsk/BggXp1atXP7QdY4wxxhhjeUlERAT17Nkzy983O4ZIW7FiBbd+/En37t0Tf161ahUVKVKEiIiGDh1Kjx8/pu7du4vb5eWu0Pk6VIyMi1V1EXI1/v4YY9/z/PnzLN2OMcYYY4yx3MLR0ZFWrFih6mL8EHUfo1EdKY+hqAgUM/u9X79+ebq1Yr4OFf+6f0XVRWCMsTwrJSUlS7djjDHGGGMst9DV1eXWf/lAo0aNMl3u6+tLhw8fzuHS5Lx8HSryRC2/RjFRC2OMZUZfX58SEhJ+aDvGGGOMMcYYy2327dtHQ4cOzbA8PwSKRPk8VGSMMZZ97OzsKCoq6oe2Y0yVEB2v6iLkevwdMsYYYyy3yIpJdIYNG0azZ88mIqIjR46Qk5OTuE55eKdhw4bRw4cP//O/o+6T6OTLUNHY2Jh0tLW5lV0W0NHWJmNjY1UXgzGmhnR0dLJ0O8aymrGxMWnraFPSmQcEVRcmD9DW4ToBY4wxxtRfVk+iM23atG+uUwSP/5W6T6KTL0NFKysrCgoOppiYGFUX5ZsiIiJo6tSpNHr0aLUeNNXY2JisrKxUXQzGmBoKDQ3N0u0Yy2pWVlYUHKTe9QEirhMwxhhjjGWlrJxE53vhZFb8G+pc9yPKp6EiUdqNRG6o+Do6Oqp1Ks0YY98SFxeXpdsxlh1yS32AiOsEjDHGGGNZISsn0Tlx4gTdu3dPMhv0kiVLqGTJklny/uou34aKjDHGGGMs/8iK8ZPSU7xfVr8vkfqPocQYY4zllAcPHtDLly9VXYzv6tKlC61evZq6dOlCr1+/ptevX6u6SJmyt7enYsWKZdn7cajIGGOMMcbyvKweP0nZ1KlTs/w91X0MJcYYYywnvH37lvr17UfyVLmqi/JDVq9ereoifJdMQ0YbNm7Isp46HCoyxhhjjLE8LyvHT8oJ6j6GEmOMMZYTYmJiSJ4qp1b2tchC10TVxcnV3idE0/aXJykmJoZDRcYYY4wxxn5UVo6fxBhjjLGcVaZgESpkYKPqYuRqT7+8pu0vT2bpe3KoyBhjjDHGGGMsR8TExNCYMWPo7du3ZGVlRVOmTCFjY2NVF4sxpuZeff2g6iLketnxHXKoyBhjjDHGGGMs2/n7+1NkZKT4+/v376lZs2ZkZ2dH69evV2HJGGPqytjYmHS0dWjZ4xBVFyVP0NHWydIHORwqZgGeTZAxxhhjjDHGvi19oKgsMjKS/P39OVhkjGVgZWVFQcFBFBMTo+qifFdERARNnTqVRo8erdbjIhsbG2fZeIpEHCpmCZ5NkDHGGGOMMcYyFxMTIwkUBUGgFi1a0M6dOwkAEaUFizExMdwVmjGWgZWVVZYGYdnJ0dExX+UtHCpmAZ5NkDHGGGOMMcYyN2jQIPHncePG0ZQpU2jHjh2koaFBY8eOpUmTJonbrVmzRkWlZIzlF1nd2/TDhw80YcIEIiLq168fTZgwgczNzbPkvdW9pymHilmAZxNkjOU12TGsw/c8fPjwl16v7hdbxhhjLLfKijrBs2fPxJ8VASIRUWpqquT3Z8+e/VKdgOsDjLEfkZ29TZOTk2n06NFZ9n7q3tOUQ0XGGGMZZOeFNjO/+m+p+8WWMcYYyykPHjygly9fZtn7vX79mlavXp1l7/dvfqVO0KVLF7Kxscmystjb21OxYsWy7P0YY+ohq3qb9u7dm1JTU4mISE9Pj5o3b067du2i+Ph4IiLS0NCgZcuW/XJZ1RmHiowxxjLIigvtz9wU/Oq/pe4XW8YYYywnvH37lvr260epcrmqi6ISWR1+ashktHHDhlwzltuvksvldOfOHYqKiiIzMzMqXbo0yWQyVReLsSyXFb1NIyMjxUBx27ZtYnfnbt260YcPH6h169aUmppK+vr6ZGdn98tlVlccKjLGGMsgKy60q1evpi5duvzQdoUKFfqlf4sxxhhjaWQyWb4NFbNafgrUTp8+TX/99Re9fftWXGZlZUV9+vQhLy8vFZaMMfXUrVs3IiIyMjLKMH6iubk5GRoaUmxsLHXr1o0OHDigiiLmCA4VGfsJ/PSOqbO3b99STEyMqovx05KTk395TMWsZGxsnG9aJDDGGMtbrKysaF1wcJbWByIiImjq1KlZ9n7ZafTo0VnaeyG/1AlOnz5N48ePz7D87du3NH78eJo4cSIHi4ylk5iYSERE3bt3z3R9QEAALVq0SNwur+JQkbEfxE/vmDp7+/Ytdf6tMyUlJqm6KD8tJ8du/BHaOtoUHBScL24iGGOM5T1WVlZZeg3LTUOi8EQtP08ul9O0adO+u820adOoevXq3JiCMSU6OjqUkJBAq1atosaNG2dYr5jJXkdHJ6eLlqM4VGTsB/DTu9whP7ckjYmJoaTEJHIrSaSvr+rSZBQXT/Tw7v9+dytFpK+nuvJ8S1wc0cN7SRQTE8OhImOM5VJr1qyh4OBg8ffOnTtTQECACkuUu2XFkCjDhg2j2bNnExHR1KlTJTOjKv8+bNgwnngth129evVfW1IlJibS1atXqXLlyjlUKsbUX2BgIPn7+9Pnz5/pw4cPki7QHz58oNjYWHG7vIxDRcb+BT+9yx1Onz5Ny5Ytozdv3ojLrK2tqXfv3vkq8H14T9Ul+DHKASNjjDGWVWrXrp1hWXBwMAUHB9OJEydUUCJGRFSvXj0xVBw9ejRpaWlRu3btaPPmzZKAsV69eqoqYr61adOmH96OQ0XG/sfOzo5kMhnJ5XJq3bo1GRoaUkBAAK1Zs0YMFGUyWZ6epIWISEPVBWBM3aV/eqehoUHt2rUjDY3/HT6Kp3dMNU6fPk0TJkygwoUL09KlS2n//v20dOlSKly4ME2YMIFOnz6t6iJmO2NjY9LS1lJ1MfIELW0tMjY2VnUxckxMTAz179+f2rRpQ/3798+V43IyxhhRxkDRyMjou+tZzpHJZDRx4kTx9+TkZFq3bh0lJyeLyyZOnMgP6FUgNDQ0wzJTU9Mf2o6x/O7o0aPieSs2NpYWLVokCRSPHj2qyuLlCAEAVF2IrPD582cyNjammJiYDBUIxn5Fr169KCwsjIiIgoKCJIM/R0RE0G+//UZEREWLFqXly5erpIz5mVwuJ39/fypcuDBNnjxZEvampqbS2LFj6enTp7Ru3bo8X1FV94laFAO9Z/Ug6lktvwzKTkTk7+9PkZGRGZbb2dnR+vXrVVAilpmHDx9Sz549acWKFdwtkLFvUO7yPHjwYGrSpIm4bs+ePTRv3jwi4q7Qqnb69GmaNWsWxcXFicsMDAxo2LBh+apniTpRDtuHDRtGDRs2FH/fv3+/2MKUiLi1L2PfEBkZSd26daPExETS0dGhwMDAXN1C8WfyNbXo/pyYmEjjxo2jdevW0adPn8jd3Z2mTJlCPj4+qi4aywMSEhIoIiLiP79eESgq3utbs9SGhYX98gy2PLj0z7tz5w69efOGxo4dKwkUidJalXbo0IH69etHd+7cobJly6qmkDkkqwdmzy6Ojo4cjKiBbwWKRGkVI39/fw4W/4NfveZkRvF+Wf2+fM1hv+Lx48f07NmzLHu/+Ph4evLkyX9+/T///CP+/PjxY5o/f36m2wUHB1N0dPR//neIiFxcXEhPL+sGBnZ2dqYiRYpk2fupMy8vL6pevXq+HQM7q2X1NefAgQMkCALZ2dlRZGQkHThwQLKe73UYy5ydnV2G4yW/UItQ8ffff6ft27fToEGDyNXVldauXUsNGzakEydOUI0aNVRdPJbLRUREZNnssv/2Pr/67+S3VihZURG6ezdtcL6UlJRMKzpyuVzc7lduAPJbJSg3BSNE+W///KqYmJhvBooKkZGRFBMTk6+6gmeFrLzmpDd16tQsfb/8ds1hWWvJkiV069YtVRcjU8oB439Zn9PKlClDCxYsUHUxcoxMJsvzD3q/Jyt7lih6gWSVu3fvinXrzPzq9S0re6vkp54ljKkzlXd/vnz5MlWuXJlmz55NQ4cOJaK0m9lSpUqRpaUlnT9//ofeh7s/5x0PHjygly9fZtn7JScn04cPH/7z61evXi3+bG9vT4UKFaIzZ86Qp6cnPX36VFLWLl26/FJZzc3NSUsr68bFs7e3p2LFimXZ+2V199qsrghlp6zusqvuFSFFd8vcgsORn5PZuGL6+vqS7mgK3NXp52RHIJ9dOIxnv0KdWyo2bdr0p9f/DG6pyP6rt2/fUufOnSkpKUnVRcn1tLW1KTg4WK3r04zlVrmq+/P27dtJJpNRjx49xGW6urrUtWtXGjVqFL148YIcHBxUWEKWk96+fUv9+vYleWqqqouSqZcvX4oh4pkzZzKsVw4g1YFMQ4M2bNyYJRfbt2/fUudOnShJaUDt/CSrw09tLS0KXrdObStCjo6OtGLFClUX44ep8xiN6m7gwIHUrFkz8feQkBBauHCh6gqUy+nq6nLAzfKFIkWKqFUQZmJiIo6pWKRIkQxjKirwmIpM1RS9aNiv4e+RMfWg8lDxxo0b5ObmliH9rFSpEhER3bx5M9NQMTExUTIj7+fPn7O3oCzHyGQytQ0Vc5usHJ8mJiYm3waK2SEpOZliYmLUNlTkYCRrqVuLHmVPnz795thjRPTddT+CW/QwxnJCQECAGCrOmzeP5s2bl2kLbA4UmSpZWVnRkiVLsqxX1uvXr9WuUcP3dOnShWxsbLLkvezt7dW2Hs1YfqLyUPH169eZnlgUy169epXp66ZPn04TJ07M1rKxnGdlZUXB69ZxF9sskpVdbI2NjUlbS4uDxSyiraXFY9XlIzz2WNbJb2OPMcZ+3IkTJyTDO6QPFHk4B6YOihUrlmXDEyUkJFDlypWz5L2+N+xNVvVe4WE3GMt7VD6moouLCxUtWpT2798vWR4eHk4uLi40f/58GjRoUIbXZdZS0cHBgcdUZBlk5fhW6QPKrA4B1f1Cm9VjKiYlJdGbN2+y7P1SU1PpxYsX9OXLFzIwMCAHB4cMM0L/V9bW1qStrZ0l70Wk/mMqsqylbi0V0weF5ubmVLRoUQoLC8swBi2PPcYYy23WrFkjtlok4i7PjP2oTZs20cqVK8Xfe/ToQe3bt1dhiRhjqvAzYyqqPFQsVaoUWVlZ0bFjxyTLQ0NDqWTJkrR8+fIfmiyAJ2phjDHGfsyKFSto8+bN/7pdu3btctWEPYwxxhhjjLFf8zP5WtY04fkFNjY29Pr16wzLFctsbW1zukiMMcZYnvajQSEHiowxxhhjjLFvUXmoWLZsWXr48GGGiVYuXbokrmeMMcZY1vq3scV47DHGGGOMMcbY96g8VGzVqhXJ5XLJ2A2JiYm0Zs0aqly5cqYzPzPGGGPs1504cYLatWsnWdauXTsOFBljjDHGGGP/SuVjKhIRtWnThnbt2kV//PEHFSlShIKCgujy5ct07Ngx8vLy+qH34DEVGWOMMcYYY4wxxhj7734mX9PMoTJ9V3BwMI0dO5bWrVtHnz59Ind3d9q7d+8PB4qMMcYYY4wxxhhjjLGcoxYtFbMCt1RkjDHGGGOMMcYYY+y/y1WzPzPGGGOMMcYYY4wxxnIXDhUZY4wxxhhjjDHGGGM/hUNFxhhjjDHGGGOMMcbYT+FQkTHGGGOMMcYYY4wx9lM4VGSMMcYYY4wxxhhjjP0UDhUZY4wxxhhjjDHGGGM/hUNFxhhjjDHGGGOMMcbYT+FQkTHGGGOMMcYYY4wx9lM4VGSMMcYYY4wxxhhjjP0UDhUZY4wxxhhjjDHGGGM/hUNFxhhjjDHGGGOMMcbYT+FQkTHGGGOMMcYYY4wx9lM4VGSMMcYYY4wxxhhjjP0UDhUZY4wxxhhjjDHGGGM/hUNFxhhjjDHGGGOMMcbYT+FQkTHGGGOMMcYYY4wx9lM4VGSMMcYYY4wxxhhjjP0UDhUZY4wxxhhjjDHGGGM/hUNFxhhjjDHGGGOMMcbYT+FQkTHGGGOMMcYYY4wx9lM4VGSMMcYYY4wxxhhjjP0UDhUZY4wxxhhjjDHGGGM/RVPVBcgqAIiI6PPnzyouCWOMMcYYY4wxxhhjuY8iV1PkbN+TZ0LF2NhYIiJycHBQcUkYY4wxxhhjjDHGGMu9YmNjydjY+LvbCPiR6DEXSE1NpVevXpGhoSEJgqDq4vyyz58/k4ODA7148YKMjIxUXRymhPeNeuP9o75436gv3jfqjfeP+uJ9o75436g33j/qi/eN+uJ9o97y0v4BQLGxsWRra0saGt8fNTHPtFTU0NAge3t7VRcjyxkZGeX6P8i8iveNeuP9o75436gv3jfqjfeP+uJ9o75436g33j/qi/eN+uJ9o97yyv75txaKCjxRC2OMMcYYY4wxxhhj7KdwqMgYY4wxxhhjjDHGGPspHCqqKR0dHRo/fjzp6OiouigsHd436o33j/rifaO+eN+oN94/6ov3jfrifaPeeP+oL9436ov3jXrLr/snz0zUwhhjjDHGGGOMMcYYyxncUpExxhhjjDHGGGOMMfZTOFRkjDHGGGOMMcYYY4z9FA4VGWOMMcYYY4wxxhhjP4VDRcYYY4wxxhhjjDHG2E/hUJExxv7F9evXiYiI57VijDHGGGOMMcbScKjIJFJSUlRdBPYfceCVPSZOnEgVKlSgAwcOkCAI/D3nIXK5nIj42GGMMcYYY3nX9OnT6fHjx6ouBsujOFRkEpqamkSUduJ58uSJikvDfpRcLidBEFRdjDypdu3a5OPjQ02aNKH9+/dzsJhHACCZTEZERJ07d6Z9+/apuER5Gx8z+Zdi36empqq4JIz9HH7Qnv0+f/5MT58+VXUxGMvTtm7dSuPHj6chQ4bQs2fPVF0cloncfr3hUJFlsGvXLho9ejSFh4cTEd8I5AaKcKRr1660bt06FZcmb/Hy8qKJEydSzZo1qXHjxrRv3z4OFnO51NRUMYTv27cvHTt2jN6+fZvrL+jqRNEKNC4ujoiIj5l8SPE3kJSUREREX758UWVx2DdwHe/bFA/aQ0JCKDk5mb+rLJaQkEBVq1albt260aNHj1RdHJYDFNcFlrPatGlD48aNo9u3b1P//v05yFdDiuvNP//8o+KS/DccKrIMmjdvTlWqVKHx48dTcnIyaWjwn0lu8OzZMwoODqbLly8TEbcMygqKG4gqVarQtGnTqHHjxtSkSRM6evQohyS5FADxnJacnEyfPn2i3r17U5s2bcQLOvs1crmcZDIZPXr0iDp27EgBAQFERNyaOh9R/A08fPiQAgICqGzZslS5cmWaPHkyhYaGqrp47P/J5XLS0NCgV69e0datW2nBggW0b98++vz5s6qLpjYWLVpELVq0oI8fP5KGhgZf97OQhoYG9ejRgy5fvkwjRoyghw8fqrpILBulpKSIjSD27dtHb968UXGJ8gdFkDtmzBjq0qUL3bp1iwYMGMDBohpasmQJNWvWTBzLPzfhtIhlAICaN29OoaGhdPjwYXEZU2/Ozs40btw4WrFiBV27do1v4LOA4u/+xYsX9ODBA7Ey5Ovry8FiLqU4Ltq1a0fOzs5069YtqlWrFhkYGPC+zAKpqakkk8koNDSUvLy86NOnT6Svr6/qYrEcpBha4MGDB1S9enUKCwujIkWKUOXKlWnixIkUEBAg1i2Y6iiO1Xv37pGXlxf16dOHxo4dS02aNKH27dtTSEiIqouoFjw9PcnKyopmzJhBKSkpXLfKQtra2tSrVy+aN28e7du3j0aMGEFhYWGqLhbLBnK5XHxw27FjR/rtt99o5MiRlJycrOKS5X0ymUxsJDF27Fjq3r07B4tqys3NjUxMTGj//v1ElLta9nKomI9l9oeq6BbYq1cvMjQ0pLVr1xIRtzBRN8rhh3J3HF9fX7KwsKB169ZRUlIShyS/SHFj7OHhQYGBgZSamkqdOnUimUxGvr6+PHlLLhUfH0/FixcnHR0devDgAbeOyEIaGhoUGRlJLVq0IA8PD5ozZw4tWbJE1cVi2Uz5OiQIAn3+/Jn69+9PpUuXpuXLl9P27dtp7dq11LBhQwoPD+fzphrQ0NCgFy9eUMOGDcnZ2ZnWrl1Ld+/epb1799KBAwfo77//pufPn6u6mCpXunRpqlSpEu3fv5+ioqKIiLuM/yrlY19HR4c6duxIgYGBtHfvXpo+fTrdv39fhaVj2UHxUL5Zs2Z08uRJmjJlCo0bN460tLTEbfiakH00NDTEIX7Gjh1L3bp142BRDfn6+lKjRo1o6dKlFBsbSzKZLNccFxwq5mOKE/zYsWNp/fr1RERit0BDQ0MaPnw4HTx4MNf27c+rlMeDIyJJ9/TKlStT/fr1aevWrRQTE8M3bj8hISGBiKSVmoSEBOrXrx9ZWFjQggULaPfu3RQUFEQhISFUtWpVatSoER08eJC/51wEAOnp6dGAAQOoX79+ZG5uTitXrqQPHz7wfswiFy5coPfv31PPnj2pYsWKRJTW2vfcuXM0Y8YMunr1KnevzCNmzpxJz58/Jw0NDUnQEhcXR3fv3qWGDRuKfwNDhw6lgwcP0rRp06hSpUokCAK3Uskhr1+/JqL/Xd8U/w8JCSFtbW0aO3YsNW7cmJycnOjGjRskk8moVatWZGlpqbIy57TMxtRNTk4mTU1Nmjp1KkVGRtKyZcuIiHhYoP9IcbwrX2sV1+QKFSqQrq4uBQcH07hx4/hhXx60cOFCunTpEs2bN4/8/f2pUKFC4jrF2Lssaylfl5WH+Bk3bhx17dqVg0UV+db1hoioV69elJSURLNnzyYAuadhF1iel5qaKvn969ev4s83b96Enp4eBEGAr68vli9fjsTERABAaGgo7Ozs0KNHDwCAXC7PuUKzTCnvy06dOqFVq1YICwvD58+fxeWhoaGwsLBA3759VVHEXKlz584YN24cvnz5IlkeExODwoULo1OnThlec+LECZQqVQqCIOD48eMA+BhRRykpKd9c9/HjR8yZMwf6+vqoVasW4uPjAWQ8Z7KfM3fuXAiCgPDwcADAli1b0KBBA2hra0MQBJiamiIwMFDFpWS/auPGjdDU1ESLFi3w8uVLAGnnwNTUVFy8eBG6urq4efMmAGDIkCHQ1NTEypUrxeNMLpdj48aNSE5OVtlnyA969OiBChUq4OHDhwDSzm+Kc1yPHj1QtmxZcdthw4ZBS0sLK1euRGxsLIC0OqPi5/wgODgYV69elSyLiopC27ZtUbJkSTx+/FhFJcvdPnz4gMaNG2PHjh3iMsWxHx4eDgcHB3h5eWHSpEnQ0dFBixYtEBYWpqrismzQr18/uLm54cWLFwCAV69eITAwELVq1UKpUqUwatQoREREqLiUeYei/vvu3TucO3cOW7duxYMHDyT3jOPHj4eDgwMaN24s1tlYzgkKCsLTp08ly+Li4lCzZk2UK1cOMTExAHLHfQmHivlEYmIitmzZgrdv34rLfH198fjxY7x69Qq7du1C6dKlYW5ujpIlS2LTpk34/PkzAgMDIZPJcOfOHRWWPn9TBFXKN14vXrxAixYtYG9vDxMTE7Ro0QIXLlxAamoqkpKS0L59e7i6uuL58+cAcsfJSJWcnJxgbW2NN2/eSJZ//foVbm5uaNCggbhMeT8sWbIEgiBAW1sbISEhOVZe9mOU99XatWsxdepUTJ48GS9evBAfnnz8+BFz586Fnp4eateujbi4OAB8zPwMxXel+P/jx49ha2uLIkWKoEqVKpDJZPDy8sKyZcvw5MkTeHp6olSpUkhISFBlsVkWGDt2LJycnNCsWTPxRhEAYmNj4ezsjD59+mDs2LHQ0tLC8uXLxUARAMaNGwcbG5sMFWqWtUaNGgVnZ2c0aNBADBYV9YqBAwfC1dUVADBmzBgxUFTeT40aNcKiRYtyvuDZJP25Xfk8tGPHDgiCADs7OwwbNgyhoaHid7Vnzx4IgoC1a9cC4IeIP2v//v0QBAHly5fH3r17xeWPHj0SA8UnT54gISEBgYGBHCzmQb1794aZmRl2796Nf/75B+XLl4e9vT3Kly+PunXrQhAELF68WNXFzBMU56d79+6hbNmyMDExgSAI0NfXR5cuXXDq1ClxW+Vgka/HWSv99Ub5vmT9+vUQBAFFihRB9+7d8erVK7Fxy5kzZ6ClpYV58+blaHl/BYeK+cT9+/fh5eUFOzs7fP78GU2bNoWmpiZ2794tbhMTE4PNmzfD19cXenp6cHV1Rbdu3WBra4vu3btLKpksZyQnJyMwMBCbN28Wl1WsWBELFizA169fERMTg8GDB8Pd3R2ampro0KEDDh06hPDwcGhqamL+/PmqK3wuoHxTEBoaCiAtZFKc1BMTE9GpUycYGhpi586d4raKm5CPHz+iaNGiKFKkCExNTREbG8thlJpQ3rd+fn4wMTGBiYkJ9PT04OzsjNWrVyM6OhqANFj08fERg0X2fYqn4ElJSQAgBrUAcOjQITRq1AjVq1dHcHAwHj16JK7r1asXihUrJnlaznIX5RbAo0ePhoODgyRYjIuLw4ABA2BoaAhNTc0MLVPPnz8Pb29vtGzZkv8OsonytWjGjBlwdHRE/fr1JSHNgQMHYGNjg4oVK0ImkyEoKEg8LwLA4cOH4ebmhjlz5uSpEC0xMRGnT5/Gx48fxWVDhgzBkydPcPr0aQwYMAB6enpwdHRE27Zt8fDhQyQnJ+O3336Dq6ur2DKX/Zxt27bB3Nwc7u7uOH78ON6/fw8HBwd4enqKgTeQ9kB35cqV0NHRQevWrXH//n0Vlpr9rG/1EImMjIS7uzsEQYAgCPD29sbKlSvF9aVKlYKPj893e5iwH/f06VPY2NigZs2aWL58OXbs2IEBAwZAEARUrFgRBw8eFLedOHEiChcuDE9PTzx79kyFpc57EhIScOnSJcmD19mzZ+PEiRO4f/8+/P39YWtrCysrK/Tv3x+XLl1CYmIimjZtiurVq+eaoJdDxTzswYMH+PTpE4C0ClRgYCBMTU1haGgICwsLnD59WuzSkv4EvmXLFnTr1g2ampoQBAFFixbF69evAfDT2Zz0/v17DBo0CIIgIDAwEA0aNEDBggWxZcsW8UYeSLtwLF26FEWLFoW2tjaqV68OR0dHuLi4cGXsXyg/NXr27Bns7e0xc+ZM8Ub3zp07MDAwQMWKFXH48GHJa7ds2YKSJUti7969fJOhpjp16gRbW1usWrUK9+/fR0xMDEqUKIFixYph0aJFYteCjx8/Yv78+ZDJZChfvjxXav+F4vt5+PAhOnbsCA8PD5QoUQJTp04VA3pAGjQCwPXr11G1alW0bt2aWyrmcsr7b+TIkXBzc0OzZs3Ec+GjR49QsWJF6OvrY8iQIZDL5UhISMCBAwfg4+MDW1tbPHjwQFXFzxeUz2MzZ85EoUKFUK9ePTHA+fDhA/z8/CAIAho2bCh57aVLl+Dr64tSpUrluS6JJ06cQLVq1fDbb78hMTERvr6+sLGxwblz58RtIiIi0LdvX7i5ucHQ0BBdunRBmzZtUKJECaxfvx4A14d/lHLAvXXrVpibm6N48eKwtLSEt7c3Hj9+nOG7VLRYFAQBPj4+4v0MU2/KdeoLFy5g165duHjxonhd+PLlC4KCgnDgwAHJkEPh4eHw8PBAv379+OH8L1J8fxMmTICNjQ1OnjwpWb9mzRro6OigVq1auHfvnrh8+PDhefJ8rwrKf8Nnz56Fu7s7hgwZgnfv3qF27dqwtbXFxYsXAQDx8fF4/PgxevfuDRcXF8hkMgwfPhwtW7aEvr4+9u/fD0D9rzccKuZRp06dgiAImDFjhqSFoYeHBwRBgLW1tZiYK4dT6f9gb968iZ49e0JfXx+9e/fOmcLnc0eOHMGFCxfE3x88eIDWrVtDJpPB2NgYBw4cEG/m0gcf4eHh2LlzJypVqoQCBQpAS0sLmzZtAqD+JyN1oegmsHDhQrESu3fvXujp6aF48eKYPXs2oqKisGvXLtSvXx+1a9fOMBYjUw+7d++Gi4sLli9fLobEFy9ehJ6enthycfHixWKwGBUVhSlTpmDJkiWqLLbaU1SWQkNDYW5uDg8PD7Rs2RLt27eHlpYWypUrh6NHj2Z43Z49e+Dr6wtLS0sOk3I5xbXn7t27GDJkCOrUqYOCBQtCEAS0bNlSHHojNDQU3t7e0NHRgYODA5ydncX/3759W5UfIV9Q7KdPnz7h9u3bqF69OqysrCRdoSMiIlCjRg0UKFAAvr6+WLNmDQYNGoSKFSvC1NQ0zwx/oxxKxcfHo3379jA0NIS9vT0sLS1x5MgR8Vqu3Ar73bt3GDlyJCpVqiSOC1uvXj1VfIRcSVH3VK6Dbty4EQULFkSBAgUkLdXS12kTEhKwePFivibnEsr7uHXr1jAzMxNbJdasWRPBwcGZvu758+eYPHkyTExMsHXr1pwqbp7322+/wd7eXrxnVA58ly1bBkEQJMcfkNaYhf269MNp9erVC4IgoFChQrC0tMT+/fszfbB+584dzJ07Fw4ODihRogQEQUClSpUkPQjUFYeKedTXr1/h6+uLGTNmAEg70b969QrdunVDv379YGpqisKFC4tjLCqfaBQ3jIqLw7t379C9e3cUK1ZM0nSXZa3U1FQ8f/5cbJYeGRkprmvdurV4YVaMN6L8FCT9U72UlBRs3rwZ1atXR5EiRXLFyUgVlMerVA7Xa9euDX19fSxcuFAMnE6fPo1ChQqJ+0Emk8HS0hK3bt1SSdnZ98nlcmzduhWNGjUSu3JcvXoVenp6CAgIwIsXL+Dh4QE7OzssXrxYPEaUL/L8tPx/0k+mERMTA29vb9SqVQtXrlwRl3t7e8Pa2hr79+8Xv7/Pnz+jT58+MDc3R7FixThMyiMeP34MCwsL1K5dG5MmTcK6devg6+sLTU1NNGvWTDzuIiMjsXXrVvTs2RMBAQFYunSpGDqy7KM4/u7duwd7e3vUqFEDxYoVQ+nSpSEIAurXry+2Kn758iWGDh2K4sWLQ1tbGw4ODmjevLmk1XFutnv3bujr6+PatWuS5ZaWltDU1IS3t3eG+nD6839YWBi2bt2KsmXLQiaT4e+//86ZwudiyteN9+/f4+XLl+L3unv3bpiamsLd3V1siQNk/N6Vgyq+JucObdu2hbW1NRYsWIBbt25hz549sLKygpGREc6fPy/ZdsOGDejUqRMKFCgg3rOyX6M4Trp16wYjIyNxcim5XC7+l5KSAnd3d3h5eWW4B2K/Zs+ePTAwMMCBAwcky42MjKClpQU/Pz9xn6TPXBQePHiAwMBA1KhRA4IgiOPQqnMDIQ4V85ioqCjxZ+Unfvv27RN/j4+Px8qVK2FqaopChQrh3bt3AP538Vdsl5KSIv584cIFCIKAf/75J0c+R362bt06TJ48Wfw9NTUV69atw8qVK9GsWTMIgoBly5Z98/WKfZacnIwtW7bAxMQE27ZtE9+LpVF8T+Hh4Rg6dCjWrl0rmciodu3a0NPTw6JFi8TA6cWLFzh06BBmz56NdevW8bgjaiSz7sovXrwQZ7OLiIiAg4MDmjZtKi5bvHgxBEGAra0tZs+ezZWqTEydOjXTltHPnj2DtbW1ZNzWYcOGQVNTE6tWrRKPmeTkZMTFxWHNmjWYO3cuh0m5kKKOoCwlJQWDBw+GqakpTp8+LVk3ePBgWFhYoHnz5vwgUsWioqJQunRpVKlSBefPn0dCQgISExMxaNAgWFpaon79+mKr4a9fvyI+Ph43btyQjC2cFxw5cgTW1tbihDPJycm4desWSpYsCU9PTxgZGaFLly5ia0blG7f0N3FPnz6Fu7s7WrVqlWPlz42Uv7euXbvCzc0N1tbWqFq1qlgnPXDggBgsKk/ewnXV3OvSpUsoVKgQFi1aJA6xdevWLWhpaaFv376SBhMPHjxAtWrV4O7uLmkxp87BiTr61vd14cIF6OrqokePHuIy5aC/YsWKqFWrVraXL7/ZvHkzHB0dxUZAcrkcjx49QoUKFVC7dm0IgoBhw4ZJ6sPfaiiUmJiIkiVLZhieRB1xqJiHbNmyBRUqVBCfxCr+KOfOnQtBELB06VJxUOqkpCQsW7YMZmZmkmDx48ePmD17tmRWKAA4efIkTE1N+clsNvlW96KJEyfi+vXr4u+3b99GixYtMg0W4+PjMzzxSE5OhqmpKQYPHpxNJc+dlGdFc3R0RLFixTB27FjJOkDaYpFbe+YOgwcPxuHDh8UATHFMrF+/Hra2tpJz24oVK1C7dm2UKFGCZxzMxObNmyEIApo0aZKh+8zFixdhbGyMy5cvA0ib5CCzmWM3bNiAhIQEyUMqlnt07doVrq6umT5AadeuHZydncXfv379Kv7cvXt3CIKAFi1aiMFi+mOSZZ1x48Zl2mr+5s2bMDIywtSpUzN9jZaWFho0aCCZSCkv3tDL5XLJDZxi4Pu4uDjExsbC398fRkZG+P3338Vrffpuu3K5XHzwtGrVKgiCwK2uv0H5GG/bti1MTEzQpk0bBAQEoHjx4hAEAT179sTXr1/FYLFMmTLYt2+fCkvN/ov05/OtW7dCU1MTd+/eBQAcO3YMenp6aN++vWTs8VevXgFIe7Cf188/2SU1NVW8rr5+/RqnT5/G9evXxfPUu3fv0KdPHwiCgIEDB0pee+nSJbi6uqJnz55ITk7m63IWUA5slYf4UVwnYmJiIJfL0bt3bwiCgMGDB2cYv1L5718xJvnChQthZGQk6RWkjjhUzEPmzJkDW1tb1KlTBzdu3BCXf/36FR06dIC+vj6WLl0qtmZMTk4Wg0UXFxds2rQJI0eOhCAImDJlivj6L1++ICAgAIIg5JoZiHKTo0ePQiaTYebMmeKylJQUsXVow4YNxRt3IG2ig/TB4qdPnzBlyhQMGTJEEizevn0bLi4u6NevH1800omMjISrqytq164tGcMSkF4YFMHikiVLxK7QTH0oB1Xv3r2DIAjw9fXFyZMnJX/vU6dOhZ6enhjSv3r1Cu3bt8eoUaNyvMy5RWxsLCZMmABbW1s0btxY0jX8/fv3sLCwwJ9//olp06ZBS0sLy5cvlwSKI0eORNGiRbl1Yi6laCXv6OiYYcKv5ORk9O3bFwYGBpIHX4pzZ2xsLNzc3GBvb49atWrxRFbZ6M6dO+J5TznYVazT0dHBtGnTxGXK17dWrVpBEAQ0btw4z3R1Ti99vWfQoEGQyWSSQDAhIQEdOnQQg0XlCbw2b96cITwMCgqCkZFRhroDk94UR0dHw8PDA8uWLRP/7hISEsR7ir59+wIAduzYAWtraxQvXhy7du1SRbHZT1AcU5n17ti8eTNMTU0RGxuLM2fOoECBAujYsaMYIgLA6tWr0b9/f7ElY/r3Zd/m5+eHQYMGAfjf93X37l24uLhAW1sbBQsWhKenp9jzKiwsDP7+/hAEAXXq1MHcuXMxZcoUVK9eHaampggLC1PZZ8lLAgMDMW7cuAwNUIYMGQJtbW2cOHFCsrxHjx5isKh48Prp0yfs2bNHnMBFISgoCLq6ump/veFQMY9ZunQpChUqhJo1a0oq+gkJCWjfvj10dXUzBIuBgYEoWrQoZDIZDA0NMWvWLMl7JiYmYvv27fxENptcu3YNjRo1gqWlJebOnStZt2PHDjFYvHTpkrj8+vXraNmyJWQyGTp37ozff/8dgiBIAhK5XI6pU6dCEIQ8e7PwXyguwmvWrIGFhYWkAqtcGVa+8fLx8YEgCFixYgVXetSIcqA4cuRIDBgwAK6uruKg4KdPnxb31+HDhyEIAvz9/bFgwQIMHDgQenp6CAwMFN+D9+3/KI6F2NhYjBs3DjY2NmjUqJEYLCYkJODPP/+Erq4uBEHA+vXrJTcYFy5cgLe3N1q3bi1OksNyn5SUFLFL6PPnzyUtFg8ePAhBEDBixIgMN4fx8fFwdXWFu7s73N3duRt0NpLL5Thx4gSuXr0KQHqj/+rVK9jY2GQIdhXh444dO2BqagoLCwu0atUqzw8BkZqaikOHDsHDwwNOTk6Sem1iYiI6dOgAY2NjdOzYERcvXsT06dOhra0tmWDiw4cP6NChAwwNDSVdOZlU06ZN4e/vjypVqoh/e8r1qt9//x1aWlo4cuQIgLTeBDKZDBs2bFBJedmPSUpKwpo1a8T9BgA1atQQe3u8e/cORkZG8PX1hYGBATp37ixpjfX48WN4enrC39+f6wY/KTIyUhwGa8KECQDSHvCWKFECXl5eWLhwIUaOHAl7e3s4OTmJrdfDw8Mxf/58FCpUCDKZDObm5qhatWqemYRL1ZKSktC/f38IgoDp06dLhg7ZuHEjKlWqBGdn5wzBYs+ePcVWpIcOHcL06dOhqamJnTt3itu8evUKbdq0gaWlpdrXozhUzCOUw5DFixfDxcXlh4NFuVyO0NBQhISE4Pjx45m+J8teN2/eRPPmzVGwYEHMmTNHsu5bweKtW7fQv39/GBkZwdnZWTK2mSIcef/+vTgYLJMaMmQIjI2N8fr1awDSQEm5+7hC06ZNM7TWYeqhSZMmsLOzQ79+/RAcHIxevXrBzMwM5cuXx6lTp8TwcdWqVdDX14e2tjYsLS0zHGtMStH14vPnzxg7dizs7e3RrFkzcfmVK1fg5eUFPT09TJw4EUDasbN//374+vrCxsaGZ3nOI2JjY+Hi4oLKlSvjyZMn4vlywIABkMlkmDJlimS2wwsXLsDLywu3bt2SjPXMsldYWBgaNmyIu3fvivtoxYoV0NDQwMiRIzO0opg9eza8vb0xa9YsSRfEvEL5wZOiFXVKSgpOnTqFMmXKwMHBIUOwGBAQAH19fRQoUAAFChSQjHENpE1qM3jwYJ6k7Ts+ffoEHx8f6OrqwtDQUPKAT1Gvev/+Pezs7NCoUSPxdXnxbzCvefv2Lbp06SJOHtGwYUMYGRlhx44dSEpKglwux4QJE2BqagpnZ2dJcBgREYGxY8fC0tIS27dvV+GnyL3CwsLQtWtXCIKAyZMn482bN6hWrRqOHj0qbnPkyBGUKVMG9vb2kvNUTEwMzpw5gwcPHvB1OYt9+vQJI0aMgIaGBqZOnSr5u9+1axcqVKgAR0fHDMFi3759IQgCDA0Noa2tLYbFCtHR0fD398fNmzdz4mP8Eg4V8wjl8CMqKgqdOnUSWywqP4lIHywqxlhMjwPFnKPYdzdv3kTbtm1hbm4uCQiBbweLSUlJePLkiSTs4n33Y6ZPnw5dXV0x9FB8b8rh4v79+yU3ykz97N69GwUKFMCiRYsQFxcHIO08d+rUKdjY2KB8+fI4efKkeHN5584dXLt2TVLR4mMmI8X3de/ePYwePRpVqlSBra2tOE6eosXi6dOnUb9+fQiCACcnJzg6OsLe3h7Ozs7cuj2XS99ye9u2bbCysoKvr6842dGzZ88QEBAADQ0NNGrUCIsXL8b8+fNRo0YN2NnZ8fkzh506dQra2tqoWbOm2EPh06dP6NevHzQ0NNCvXz+xC9Xp06dRt25d9OjRI0+20lauF8+aNQtjx46VXO9PnjyZabCYlJSE9evXY968eZIWI5mNdcW+7dWrV/j999+hoaGBoUOHisuV/9Zq1aoFDw+PDN8nX5PV29WrV+Ht7S12tz148KBkeJTw8HAxeGzSpAnWrVuHVatWoXXr1tDS0srQI479nLCwMHEIAU9PT9SuXVuyXi6Xiw9O7O3tuS6WQ6KjozFs2DAxWFQeMmvnzp3fDBaDg4OxYMEChISEiMvkcrl4rswt45FzqJgHKF+g/fz8UKVKFdjY2Ei6ASon3Ipg0dDQEIsWLRK7NrGcp3yiuHTpEvr37w83NzfIZLIME7EoB4vfGqw1L94YZDXFjcbJkydRsGBBNG3aVHyipFwp+vvvv1G5cmVxsGmmnoKCgiAIgtjKWvkYOH78OExMTFC7dm0cO3Ys0wsz37z8T/rzx6NHj2BhYYG6deti5MiR2Lp1Kzw9PaGtrY1GjRqJXSifPHmC3bt3o2vXrujatSuWLFnC4yjmcoq/hcePH4tjKScnJ2P37t0wNTWFj4+P2BX63bt3mDNnDkxMTKChoQF9fX0ULVqUb2RygOKcpjyR0rFjx2Bra4tq1aqJ42VFRkZi1KhR0NDQgI6ODuzs7FCwYEGYmZnlyS5wyuf15s2bw8bGBt26dZN0H/tesPi992M/7vXr12jbti0EQcCMGTMyrKtQoQK8vb0lEw2y3KFx48YQBAHa2trYsmULAGkQ8vz5c8ycORP29vbQ0dGBtrY2KlasiL/++kt8Dz6u/ruwsDB0794denp6KFy4MMLDwyGXyyUTSynOb4ULF5bMtcCyz38NFpXl1uOCQ8U8pHfv3jA2NkZQUBCePHmCjx8/Yvjw4TAzM8s0WFQM0M1dOFSvYcOGcHV1hYeHB1q1aiVW/OfNmyfZbufOnZDJZKhbt67aD9iqDhQ3XPHx8YiJiRFbsgHSbk5du3aVTDBx5coVNGzYEJUqVRIHO2aql1koqAjbFS1KlFunfPnyBc2bN4cgCJlOyMPSZDZOi1wuxx9//AEzMzOcOXNGXP7lyxcMHToUpqamaNq0qSSIZ3lLVFQUzM3NMWbMGHFZSkpKpsEikDaj7uXLl3HhwgW8e/dOFUXOlx48eICqVauK16rU1FQcPXoUtra2qFq1qmQg/jNnzmDgwIHw9/fH0KFD8fDhQ1UVO0e0b98ednZ2WLduHd6/f59hfUpKCk6cOIEyZcqgUKFCXB/OBm/evEHr1q0hCAJ69eqFXbt24ejRoxg0aBAEQcCqVatUXUT2E1JTUxEbG4vFixdj4cKF8PLygq6uLnbs2CGuVw5FYmJicPbsWdy+fVsyWUtuDU5UTTl8v3v3rjjhx/Dhw8XlysHi6dOn4ejoiFKlSnEL6xzyI8Fi4cKFcezYMRWWMutxqJjL/PPPP+Jg3Mrev38PDw8PNG7cOMPsf3PmzIGRkVGGrtBfv37FgQMHsr3MLM23TuZTpkyBtrY2Nm7cKLYaPXnyJLy9vWFsbJyhK/T27dshCIKkmTTLSBFAPXjwAA0bNoSTkxPs7OwwYsQI8Yndly9f0KxZM+jo6KB48eKYOHEiunXrBnd3dxQsWDBPtuDIC86ePSv+HBkZCQ8PDxQqVEgcDF75WJswYQJq1KgBa2treHl5iccYt4pI07FjR5QtWzbDeKGpqalo1qwZXF1dxWWKAPHz589i16bWrVuL37dikgf+bvOGr1+/wtHREf3795csVw4WfX19edxeFZs8eTJ0dXXx4cMHcVn6YPH+/fsZxgrOLV2q/quzZ8/Czs4Oc+bMEceSjI+Px5s3b3DkyBGxZWJycjJOnjyJ4sWLw9DQUPI9sqzx5s0btGvXDpqamtDU1ESdOnXEiSUU+LqhvjILABXX+wsXLqBWrVrQ1dWVDBcAQPIgXxnv65/zvQD2zp07YlfocePGZXiNXC7HuXPn8OTJk2wvZ37zvWvox48fvxkshoSEoEyZMjAyMsLLly/zzPHAoWIu8u7dO5iYmMDa2jpD66lPnz6hSJEiaNWqlbhMucXOwIEDxenkMwsl+YlR9goKCsKECRMytN6Qy+Xo2LEjihQpIl58FfviypUrqFGjBrS0tCTdBQDwTdy/UHyH9+/fh4WFBcqWLYuePXsiICAANjY2qF27thhMxcfHY9q0aahevTq0tbXh4OCAJk2a8IzZamr8+PEQBAGbN28Wly1YsAAmJiaoXr26ZIbTyMhINGnSBDNnzsTZs2chk8kklS4GrF69Gubm5pLWTAoDBgyAgYGBpFugohL15s0b2NnZQV9fH56envwEPA9QrtgmJycjOTkZzZs3R6VKlcRlCsrBYsOGDXmCBRU6ffo0NDU1sXfvXsly5WCxWrVqmT44yMuOHDkCQRCwf/9+AGnjfw4ePBiFChWChoYGBEEQZ3VOTk7GkSNHsH79elUWOU97/fo1OnfuDE1NTUyaNEmyju9B1Jfyef/WrVs4deqUZDZnIK0FdPpgMTo6GtOmTcPMmTNztLx5jaLOFR4ejjFjxsDPzw99+vTBihUrxG3u3buXabCY1x8cqZLycXH69Gls27YNV69elUyGFhUV9c1gcfPmzdi0aVOOljm7caiYi6SmpuLEiRNYunRphnWxsbHw9PSEs7OzpOKouNELCwuDra0tnJ2d4eHhwV2TcliHDh0gCEKm3S/9/f1hbW0ttjBVvggcOnQIgiBAX19fMrCx4maAK2Lf9u7dO1SvXh0NGjTA5cuXxeU+Pj7Q1tZG1apVxWAxJSUFKSkpuH//PmJiYhAbG6uqYrN/cfDgQdSpUwfW1tbYsGGDuHzEiBEwMTGBra0tli1bhpUrV6J3797Q19fHpk2bkJSUhCpVqqBixYr4+PFjnr+h/hmKCbsiIiIkszWvX78egiBg5MiRYtdBxfeWmJiIokWLwsvLC6VKlcpwk8FyF8W1RLnSC6T1dNDV1RVnilS+5qSkpGDPnj3i5D3KlWyWPTK7SQwNDYWmpqbkJlNBESw6OjqiZMmSeba7s/L5XPHz1atXUbJkSbi7u2PIkCGwsbGBi4sLOnXqhA0bNsDb2xvm5uaZfidct8oer169ErtCz507FwB/1+pM+XzTqVMnWFlZQRAEGBsbY+LEiRmGVqhduzb09PQwePBg9OzZE4IgYOzYsaooep6gOJfdu3cP1tbWsLW1RdGiRWFsbAxBENCgQQOxxejdu3fFYHHixImqLHaep3xctGnTBiYmJhAEAYIgoEOHDjh16pS4XjlYnD59eoY6FpB3zoEcKuZigwYNwpUrV8STzv79+6GlpYXevXtn6L5x7NgxODs7Y8CAAVizZo0KSpu/paamik/LAUhCqylTpkAQBKxevVq8OCjfnFWtWhXu7u6QyWS4du1azhU6lztw4AAsLS3FAaSBtOBJS0sL/v7+MDU1ReXKlSWzafNTPfWSPsBQUAwPYGlpKWlZsmLFCnh5eYkXdzMzM0yfPl1cX6dOHZQrV47HAUxHLpcjNjYWNjY2qFWrlmRyoq5du0JHRwczZsyQjL144cIFVK1aFadOnRIDJ5a7PXnyBGZmZnB1dUXdunUxefJkdOzYEYUKFcKZM2cyDK0CpF2r9u/fLwmjWfa6f/8+Jk6ciI0bN+LRo0d4+fIlKlWqhO7du4t1COXzpWIfFStWTJx0Jy9R/qzpHxYtWLAA1atXh4GBAbp06YLDhw+L66ZOnQojIyNxJnOWM5THWFy0aJGqi8N+QLNmzWBpaYkxY8Zg69atGDt2LARBQM+ePSXjkF64cAHt27eHtrY2bG1txeCY/XdRUVEoW7YsvLy8xIYQz58/x+DBg6GtrQ1PT09x23v37qFbt24QBEFS92XZo1WrVrCyssKsWbNw6dIlbNiwAfr6+qhbty4OHjwobhcVFYXhw4dDEASMGTNGvE7nNRwq5lIPHjyAvr4+PDw8cPPmTaSmpiIhIQGjR4+GhoYGevToIQZQERERGDlyJGrXri2ZjIJb6qhGy5YtMXbsWLHlT3x8PEqUKAFXV1ccPHhQEng8ePAAxYoVw7x58yQnKPbvXrx4IXlaN2vWLMhkMnFQ8FGjRkEQBHh5eeHkyZOqKib7BuVAURGyK1+IT5w4gdq1a8PS0lLSYjE2NhYnT57E+fPnJUM9nDp1Co6OjujYsWOm4Uh+pLgZVzyE2rRpEwwMDNCsWTOxy/P79+/F2TsbNWqEtWvXYuHChahZsyYcHBy41XsecubMGUybNg1du3ZF9erV4ejoCB0dHQiCgLJly6JOnTqYOHEi1q1bh7t37/IM3yoQHx8PPz8/CIIgzqhqa2sLQRBQuXJlrFixAteuXcObN28yvPZb45vlZsqB4uTJk+Hv74/p06dLBsCPjo5GZGSk5HWvX79Gr169ULFixUwnqmLZSzHGoiAIWLZsmaqLw75j/vz5KFy4MDZt2iTeQ964cQOCIEBLSwudOnWSPIiMj4/HrVu3JGFjXmmJpQq3bt2CoaEhFixYIFn+6dMnTJ06FRoaGujWrZu4/N69e+jXrx/u3buX00XNV1atWgUXFxds2LABX758AZD23WtoaMDAwADVq1eXXIeioqLQt2/fDPsxL+FQMZdI34IqJSUF58+fR5EiRVCmTBnx5P3mzRuMHz8e2traMDExQaVKlVCyZEkIgoDZs2erouhMSWpqKlq3bg1tbW3Mnj1bHBvz4sWLcHZ2RuHChbFw4UJER0cjNDQUY8eOhaOjo2QMRb44/zjlgaRtbGwwevRoMcy9cuUKzM3NYW1tDV9fX3z9+pWDdjXUvXt3dOzYMcNkIEBasFi9enWYmpqKMw9mZvfu3WjUqBFMTU0zjCuW3926dQsODg7iQ4sdO3ZAR0cHfn5+4riiycnJGD16NMzNzSEIAnR1dVGkSBHJWIss9/nW+U5xjXn58iXOnDkDbW1tFClSBI0aNRK7XQmCgJIlS/JQETlEeV9FRUUhISEBoaGhWL58OaZOnQoXFxcIggA3NzcIggATExN4enqicePG+WJCviZNmkBTUxP29vbQ0tJC4cKFsXjxYnG9cu+Pu3fvYsyYMdDR0ckwXjXLOa9evcLvv//O4Ycai4+Px6BBg9C4cWPxfuXs2bPQ09NDjx49xDGuAwICvlkf4Hr1rzl9+jQEQcCuXbsApN3/K67RHz58QL169WBnZycZTzyvtoRTFykpKZgxYwaqVasmzmh+8eJF6OnpoWvXrggMDIQgCKhWrRoOHTokvi6v95LiUDEXUK4M7dmzR+yqkZqainPnzqFw4cJwd3cXg0XFFPKtW7eGp6cn/Pz8sHr1avE9+ASfc77VnbZXr17Q1NTEzJkzxfHMrl+/jjJlykAQBGhqasLQ0JCbsGeR3bt3Q19fH2fOnBGXLVmyBHXr1sXff//NE9+oqfj4eDRt2hT29vbo169fpsGiYjZ0CwsLSYtFIO1cOHnyZJQqVQouLi6SJ+f5maJCGhcXhzZt2qBy5co4f/68uF4RLDZt2lTSAuHRo0e4dOkSLl++zC0UcznFtSkqKgqhoaE4cOAAoqKixCfuim3kcjk8PT3RunVrAGlBY2hoKFavXp3p5D4saymO1X+rt23duhV6enpYuXIlduzYgcmTJ8PX1xelSpXKk6GNct3qwoULcHFxwfr165GUlITz58+jQYMGMDMzw5w5cySvW7JkCapXrw4zM7NMx6lmOYuHnFF/ISEh4sPYhw8fwsTEBK1atcLLly8hl8tRu3ZtGBoaonPnzvyg8RcpzvfK9/137tyBvr4+2rVrJ6l3KerBISEhEAQB586dy9nC5iOZjRd948YNcTzex48fw9zcHC1atBBbvvfs2RNGRkaoWbMm/vnnnxwtr6pwqKjmlC+47dq1g7W1Nby9vcUm6OmDxZs3b4onpYSEBKSkpEi6vHArt5yjfBL666+/EBwcLA7Qmpqaip49e4rBomJMsqSkJAQGBmLkyJGYMGECQkJCxPfgffffbd++HYaGhuL4LleuXIGvry/8/f35ZkKNKJ/vFAFidHQ0AgICYGdnhz59+ojLlZ/4Va9eHSVKlIC2tjbCwsIk+/T8+fMICgrCs2fPcuhT5A4vXrzAnj17UKZMGaxduzbDcaDcYpHD2LxFcS0JDQ1FpUqVxNaHTk5OGDhwoPjgUrFdy5YtUapUKb4G5TDF+fDJkycYOHAgfHx80LhxY6xevVrsei6XyyGXy3HkyBFoaGhIBoiXy+WSkDgvmjJlCiZPnozGjRtLWs3evHkTLVu2RMGCBSXB4ogRI9CpUyds375dXMZ/1yw/U1z7/y3gTUpKQr9+/VC2bFmEhoaKr+vUqRMcHBwgCEK+aBWdXZRneR4/frykpXWvXr2gpaWF5cuXZzqZmoGBQZ6dhEudrFy5UvIwVXH9HT9+PIoVK4abN2+K+7F///6ws7ODrq4utm7dqqoi5ygOFXOJli1bwsrKCqtXr84w0LZysFimTBlxjEXl9cr/Z9lPuZLq5+cHW1tbeHl5id0HgIzBYmZjIGX2fixN+u/ke9/Ru3fvUKNGDVhZWaFo0aIoUqQIzMzMcOfOnewuJvtByiH80qVLsWLFCvHGOSYmBr///jvs7OzQt29fSaB469YtuLm5Yd68eZIQXhmf+6Ti4+NRqFAhmJqawtXVVRxTMf3TWEWw2LJlS9y8eVMVRWXZJDw8HJaWlvDy8sLMmTOxfv16NG7cGFpaWvD09JRUnKdNmwYrKytJ9yqWvRTnrNDQUFhaWsLFxQWlS5cWuzfXqVMH169fF7ePiYmBhYUFxowZAyDzlhV5zbp16yAIAmxsbNClSxcAkIyXe+vWLTFYVB7+59OnT+LPXLdi+ZXyOKOKh7UAcPToUYSEhCAqKirD8VGvXj1UrVpVPL+Eh4fD29sbFy5cwKNHj3Km4HmQ4nu+d+8eHBwcUKlSJQwaNEi8DkRHR6NevXowMjLCzJkzxRzg3Llz8Pb2hoeHB0+Yl822bNkCQRDQp08fPHnyRLKua9eucHZ2xufPnwGkPbRv0aIFtm3bhtevX6uiuCrBoWIusHPnTpiZmWHp0qViC8XMKkLnzp2Dm5sbSpQowbMEq4nffvsNFhYW2LRpkxgopg98FcHinDlzxPH+2PcpngS9evUK27dv/+7NruJYiYyMxJAhQ+Dn54eAgACerVSNKD8hVzxA6dq1qyRoj4mJwW+//QZ7e3t06NABCQkJCA8Px+TJk1GmTBlJtxC+Ufx3R44cEVsXTJky5ZvdLHft2gVBECTjWrLcI/3YSqmpqZDL5RgxYgRsbW1x+vRpyfqxY8fCxMQEjRo1EscKOn/+PARBkAw6zrLfx48fUaVKFVStWhUXL15EUlISUlJSMHr0aJiZmaFChQri8ATx8fHw8PAQu6nnF3/++SeMjIxgaWkp/r0qB6q3bt1CmzZtoK+vjwkTJqiqmIypleDgYLi5ueHgwYOSa37Tpk2hoaEBQRBgaWmJhQsXSuphzZo1g42NDQ4ePIhLly5h4sSJKFiwoGTcOK5//TcvXryAi4sLfHx8xFmegf/VyZ4+fYpmzZqJw/2UKVMGtra2MDc3527nOWTixIkQBAG9e/cWe3QAadchfX19zJs3D4cOHcLo0aNhaGgoGes9PxwXHCrmAjNmzECBAgUkf8AK6QOqs2fPwszMDOvXr8/JIrJM3LhxA3Z2dhg7dqz49DyzFlOpqano3bs3BEHAxIkT8/xArr9KEUDdu3cPJUqUQMmSJTF27Njvvib9OCUcjqinVq1awdbWFmvXrs205W50dDQGDRoEc3Nz6OjowMbGBoIgYMaMGSoobe539uxZWFpawtXVFfv27ROXpz9P7dmzhye4yYU6d+6M8uXLZzrbecuWLVG0aFHxd+XrzoABAyCTybBx40YAabNCW1paZng6z7LGlClTxLGVld2+fRvm5uaZnt9mzpwJbW1tdOvWTWx516pVK5QsWRLx8fF5rnX2927IRo0aBU1NTXh5eYkPGJWDxZs3b8LX15dnGWbs/x09ehQFChRApUqVcPjwYQDA1KlTYW9vj3nz5mHnzp3o0KEDBEHA0KFDxeFjIiMjUahQIWhpacHIyAj6+vpc//pJip4h6a1duxZGRkbihCxA5ue9xYsXo3PnzmjQoAGGDRvG3Z6zQfqhAJQfzk6YMEEMFpXH469VqxYKFCgAbW1tGBsbY+bMmTlWXnXBoWIuoJilTtESJ7OTjKKFW0pKSr5qaqvO/vnnHwiCgCNHjgCQ7rfMKvwdO3bEwoULc6x8udmTJ09gY2ODunXrYv/+/T/8Oh4KQH3t27cPtra2kjFjEhMT8fnzZ9y4cUN8qBIbG4sjR47gjz/+QO/evSWTs/B+zUhROYqPj0d0dHSGsPbEiRMoWLAgypUrJ56rAP4uc7u4uDhMnz4d1tbWGcYASkhIQKtWrcSgUHFtUvytfP36FU5OTpJWbzwxT/Y4c+YMBEGAn59fhrrd+fPnIZPJxHpBamqq5GanQ4cOsLKyErsxBgUF5ckhPdK3PDx8+DDCwsIkdd1hw4bB1NQU3t7e4veh/Lpv3cgzll+dOnVKcu3v2LEjhg4dKnnANHjwYAiCgMGDB4tdbqOjozFhwgTMnj0bu3fvFrfNDy2xflXHjh1Ro0YNREREZFg3aNAgGBsbi78rf5/pQy7FJGpcT8teymO0K19PFLOe9+7dW9LrLTg4GOvXr5f06shPxwWHimpK0UUJADZu3AhBEDBp0iQxLVc+wRw6dAjlypWTjI8B5K8/ZHV07NgxCIIgDtCa2cn/5s2b4hgMLKPM/oblcjn69+8PJycnyWzO/Peeu23atAkymUzs9vH8+XOMGTMGRYsWRYECBWBra4uDBw9KXqN8kef9n5HiOhEWFoZWrVqhePHicHFxQdOmTXHlyhVxEq/jx49zsJiHKPZ7dHS0OOzGixcvJDeLO3fuhCAIkrHmFPs7ISEBJUqUQIMGDXKw1PlTdHQ0Nm/ejCtXrgCQHnMREREwNjZGs2bNJPtO8fPu3bshCIKki1Vek36yQgsLCwiCAC0tLTRt2hR79uwR1w8fPhympqaoU6eO2BU6s+7/jLE0J0+ehImJCTw8PFCmTBmcPHkSgLQ3T2bBYnpc//p3crkca9euFc/p6YPFCRMmQFNTU7wWKCifs7Zs2ZIjZWVAQEAAChcujPPnz4vLlO85xowZA0EQ0K9fP4SGhmb6HvntuOBQUU18b9atr1+/wsPDA9bW1li/fr2kkvT69WuMHj0abm5uPKaCiqTvgq747/79+yhcuDCqVasmNpFW3s/Xr19H8eLFsXfv3m++X34WEBCANWvWZNoMvUqVKvDx8RGXKX9n/9YilKmnPXv2wNbWFs2aNcP06dPh5OSEQoUKoWXLlpg1axaKFi2KMmXK4PXr17xfvyGz7yUsLAzm5uYoWbIkOnbsiKZNm8LCwgLW1tZYsWKF2CpU0WKxcuXKkq7QLHfp1q0bVq1aJan8fvr0CQ4ODqhbt644LnNUVJTYvW358uWS7S9duoRChQph8ODBAPg8mh2OHj0qhvqK7/7Bgwdo0aKFZCKRGTNmQENDA7NmzQIgvb4tXrwYhoaGkglb8oLMbsRatWoFa2trzJw5ExcvXsTKlSthYmICa2tr3LhxQ9xu+PDhsLS0hKenJ168eJGDpWYsdzp58iQsLS0hCAKGDx8uHn/K95qKYHHAgAHfDBbZv0tOTsbWrVthaGgIPz8/SbC4f/9+CIKA0aNH48uXLxleu2nTJpQtW5bv9bNJ+uvOqVOnYGlpiapVq2YaLCYnJ6N69eooWLAgevfuzd3QwaGiWlAOTRYtWoT+/fujc+fOuHz5sli5fPLkCQoVKgRzc3MMHToUjx8/xsGDB9G/f39oa2tzt1kVSR94pb8QKJ5kdOnSRTL2wuvXrzFp0iTY2dnh+PHjOVLW3OT9+/coXrw47O3tM4w1lZCQgEqVKsHLyyvDza7y79xdT31kNqZbZoYOHQoXFxdoaGigbdu2kq41/fv3h7W1NXdj+xfKFaP4+Hg0bdoU5cqVk7SEun37Njw9PWFiYoJNmzaJx82pU6cgCAJq1aolBh4s94iLi4OhoSEcHBywadMmSYvFhQsXwsTEBC1atBBbul25cgVNmjSBTCaDv78/1qxZg8WLF8PT0xPm5uY8m2c2GTJkCARBwN9//y3ui5SUFGzduhWCIKBBgwaIjo4GkPZQoG3bthAEAePHjxe7Wp05cwbe3t4ZJqnKzVatWiWOJ6bcW+fkyZNwcnLCsmXLEBsbCyBtzGpNTU307dsXr169kpz3FAFIXm7Bydh/8a0HRGfPnoW1tTWKFCmCAwcOiMuVg8UBAwZAEIQMk3uxn5M+WHz+/Lm4rnfv3uLEnYpeBgBw7do11KtXDzVq1OA6cDZQPi5atmwp9jC8cOECzM3NUblyZUmwqNi+c+fOsLa25ons/h+HiiqmXBFq0qQJChYsCAcHBzg6OkJXVxeTJk0Snwo9f/4cVatWhaamJgRBgCAIsLa2xpw5c8T34BYFOUc5UBwxYgRq164NJycnTJgwQdJ8vWvXrhAEAe7u7vjrr7+wYMECtG3bFpqampKuZ0wqIiJCnBzi8+fPku+7TZs2MDExkZzklddPmzYNbdq04WBEDQQGBmLkyJGSClJ6yufBiIiIDGHGq1ev0KlTJ9SqVQvv37/n81w6vXv3lgyWrvg+ExMT4ezsjPbt22d4TUREBCpVqoRixYpJQt+zZ8/yzOi5kGKff/r0CUWLFoW9vT02btwoPlX/9OkTVqxYAX19fTRr1ky8Wbx79y5Gjx4NXV1dCIKAggULcmuIbJaYmIhy5crBwsJCEizGxMRgw4YNKFiwIOrWrSsOjXLr1i1069ZN3D8uLi55btbPd+/eoVq1ahAEQWwprTjPr169GgUKFBAfzB49ehR6enpo3769pDWi8hBAyrOnMsakXTfj4+PFVusKyuMrKyZvAaTBIgeKWeNbLRYfP36M9u3bQ0NDA/Xq1cPMmTMxdOhQeHh4wMzMDHfv3lVxyfMe5ePizp07KF26NDQ0NHD06FEA/wsWq1SpgrNnz4rXpefPn6Nhw4a4desW75f/x6GimujatSvs7e0RFBQknlwaNmwICwsLjBgxQnySkZCQgJMnT2L58uXYvXs3rl69Kr5Hfuu7r0rK33WjRo1gbm6O2rVro23bttDV1UXt2rUlM3hNnToVZcuWFcPgUqVKYcmSJZm+H5N6/vw5ChUqhE2bNonjvISGhsLKygo1a9ZEaGio5KJw7do11K5dG507d+ZQUQ0oZjafPHnyd1vUfOsYCAsLw9ixY1GgQAGsXLkyu4qZa4WHh6NEiRIoUqQIli5dKi5PTk7G+/fvYWJigm7duonLFBUiuVyOdevWQRAEBAYGAuCHUrmd4jwYFRUFNzc32NraYt26deJNYfpgUXncrLCwMBw/fhyXL18WJ35jWU+xjxITE1G2bFmYmpoiMDBQvMH//Pkz1q1bh4IFC6JOnTpiy7zo6GgcPnwY/v7+aNeuHUaNGiXp/ZAXnD9/Hg0aNIAgCJJhYZYvXw4bGxsAaa0WCxQogI4dO4rjJgJpweOYMWPE4RwUuG7FmPSh+x9//IHq1aujYsWK6NChA+7cuSP2sjp+/DhMTEwyjK+suFYo1x/Yr0lKShKDxSZNmogPRd69e4cZM2bA0tISmpqasLKygo+PD+7du6fiEuc9ysfFkCFD4OfnhyJFioj36oqJQM+fPw8LCwuUKFECK1aswJkzZzBy5EiYmprixIkT4nvk9+OCQ0U1cOLECbi5uWHevHlihej06dMwNDSEk5MTBEHAn3/++d1xLPL7H7Kq/PHHH3B2dsamTZvEbrrTp0+HTCZDjRo1EBISIm775s0b3L59Gw8ePJA8Xed9l5HiRJ+amop3796hatWqsLCwwM6dO5GcnAy5XI6goCAULFgQFSpUwOLFixEeHo5Vq1bB29sblpaWYitHpnpDhw6FhoYGJk6c+FNd9ebOnYt69erBxMQEM2fOFJdz+CV148YN1K5dG4ULF5Y8rACATp06wcDAINMxYV6+fAlBEDB37twcLS/LHsoV5FevXsHIyAjly5dHcHBwpsFi8+bNM7RWYdlP0TJYLpejYsWKKFSoEFasWPHNYFF5MjfFfsxL9Qbl8/nFixdRt25dCIIgTsLy4MEDCIKA9u3bw9DQEP7+/pI61MOHD1GhQgX06NGDHyQy9h3NmzeHvr4+atWqhZo1a8LQ0BBWVlZYsGABoqKiAPyvxWKlSpVw6NAhFZc49/veuTohIUESLL58+VJc9+rVKzx+/BivX7/OdIxFlnVatGgBKysrjBs3DkeOHMGUKVNQuHBhyXUoLCwMbm5uKFCgADQ1NaGtrY3p06eruOTqhUNFNXDs2DG0atUKT548AZA2gYeenh46deqEuLg4tG3bFnp6ehg9erRk7AWmWmFhYShbtiyGDRsmjn90/PhxGBoawtPTE0ZGRvDw8JDMTqigqERzOJKR4ju5fPkyfvvtNyQnJyMiIgINGzaEsbGxOE7S169fsWfPHhQrVkx8qlSgQAGUKFEiz3QJy0sGDx7808FiQEAAfH19sWnTJnFZXrqZzgqK4+XGjRuoWbMmChcujMWLF4vrt23bBnNzc1SrVg2XLl2SvHbPnj0wMTHB+vXrJe/Fch/FcREaGoqKFSvCz88PdnZ2EAQBrq6u2LBhQ6Zdodu0acPBYg5SBL/379/HunXr0L9/fwiCgGLFimHVqlWZBos+Pj5iHUOxn/PasarcavbUqVPw9vaGIAhiN8x+/fpBT08PJUqUkMyE/eLFC4wZMwY2NjaSh7iMMemDpnfv3qFYsWJYs2aNeC24e/cu6tSpgwIFCmDJkiXiA49Tp05BX18fLi4uGWYpZj9O8f2Hh4dj7NixaN26NcaPH49r166J2ygHi02bNsWzZ89UVdx86cqVK9DV1cWECRMkdaFjx47B09NTMiTHp0+fsGXLFgQGBuLgwYPitnxfkoZDRTXw5csX8alrZGQkXF1d0bhxYzFkDAkJgY6ODnR1ddGvXz9+YqEmXr16haFDh4pTyd+5cwcGBgbi+GWbNm0SJz3YuXOnKouaayhulN6/f49SpUqhaNGiYkD47NmzDMEikHYzsnfvXgQHB+P06dN48+aNSsrO/t0ff/zxQ8Gi8g2zcldMvnBnLrNgUXnyrmnTpsHIyAhubm7YsmULnj17hr1798LX1xeOjo48S2oeERERAVtbW9SpUwebNm3C7du3ERQUBGdnZ9jb22P9+vWSFouBgYEQBAGdOnVSccnzB8Vx+uDBA1hbW8PDwwM9e/ZEu3btYGtrC0tLy0y7QltaWqJSpUqSFot5iXLwMXToUDRt2lTSBe3UqVP4+PEjmjVrBkEQ0K1bN+zduxebN29Gx44doaWlxeNTM4a0Xm6ZTbDVrl079O/fH9WqVcswvEV0dDRq164NOzs7SaB15MgRLFu2LNvLnFcp6qv37t2Dra0tbGxsULRoURgYGIjdaBWUg8UWLVrwDNs56MCBA5IJiJQfWh0+fFicGV15AiNlfF/yPxwqZjPlAW4zk/5p86FDh2BpaSmZ9XT79u3w9fVFmzZtJJOyMNVTtB748OEDqlWrhjp16ogX9A8fPsDZ2RkFCxZEyZIl+SLxLxQn5tjYWBw5cgRly5bFiRMnJMeIcrC4c+fOH55VmKmPHw0W01+o81rLnKyUmpqaabA4f/58cZtFixahZMmSEAQBWlpaMDY2hqOjI27duqWiUrOstnLlSujr60vqD0BaN3cXFxc4OjpKxlj8+PEjgoKCeKiIHBQXF4eGDRuiaNGiuHjxIoC04/fZs2coXbo0LCwsJMFibGwsAgMDUahQoTzfUyWzLmiFChUSb/g+f/6MIUOGQF9fH5qamtDU1IS7u7ukZTbf4LH86v79++JDovDwcHH51atX4ezsDAMDA7i6uuLFixeQy+WSY+X8+fPQ0NDAgAEDMn1vrn/9N8+fP4ebmxt8fHzEsfdev34NU1NTlChRAosWLRK3TUhIwLZt2yAIAjp06CAZK55lnwcPHkBLSwvDhw8Xlyk/6Bo2bJj4gOuff/4BkHad4WMiIw4Vs1FwcDCqVq0qBk8/4u+//4aGhoaYiL99+xbdunVDhw4dsquYLAuEh4fDxsYGs2bNEpddunQJlStXxsKFC8WJENj3RUZGws7ODr6+vvDy8hKXK19cFcGiiYkJQkJC+MKrhpQvyArKF+AfDRbZtymPO5re9evXMw0Wb968ie3bt2PUqFEIDg7mbk15zNy5c6GlpYUbN24ASPvbUO4WbW5uDnd3d6xbty7DwPssZyQnJ6NkyZJo2rSpZBmQFvKWKFECDg4OWLlypSRYTD8BSV7zvS5o1atXF1ssAmktcg8cOIDLly9LglYOFFl+t3jxYmhra6NLly6SYDEkJAQ1atSAIAjihHepqaliPUIul8PV1RXNmzdXSbnzopSUFEyZMgUlSpTAyZMnxeWTJ0+GIAhwcHCAjY0N/vrrL3Hd169fsWvXLjx48EAVRc6XoqKiUK5cOTg7O0taIyoevs6cORMeHh4ZJhFTfpjP0nComE1SUlKwZs0aFChQAPXr1//hYDE8PBwmJiaoUqUKRowYgS5dukBXV1dy0uE/YvVz/fp1GBsbY9iwYQDSwuCRI0eibNmyku7qvO8yUm5qHh8fjxYtWkAQBOjp6eHmzZviuvQtFv38/CSD6DL1oBzynj17Fps3b8aNGzfw9u1byXZDhgyBhoYGJkyYkGEdy+j06dPiMaC4EXj06BH69euHgQMHSq4RgDRYXLBgQY6Xl+U8RSuHtWvXAvjf30lycjJSUlLQvHlzCIIACwsLbN26VZVFzZfkcjk+fPgANzc3+Pr6Qi6XZ5h4Zfv27dDU1ISHhwcWLVqUb1rj/1sXNAsLC8kYi+lx3YqxNMuXL4cgCAgICMDDhw/F5Xv37oWHhwcEQZAMIQSkjU3q4uKC9u3bIyUlhY+nLJCamoqlS5eiV69e4rKJEydCU1MTmzZtwpMnT2Bubg5HR0dJa2uW865evQp9fX1UrlxZck/55s0b/PbbbxgyZAguXLiAOnXqSMZY5ONEikPFbBQXF4eNGzeiYMGCqFu37r8Gi4pK5cWLF1G4cGEYGRnByclJckPIf8Dqq3nz5tDT00OtWrXg7e0NLS0tnlX1G549eyZ2BQDSumz27t0bQFrXAMXg9SNGjJC0ZFP++w8PD0fbtm35iZ4aUW6h2Lp1axgZGUEQBMhkMrRo0UK8YVRQBIuTJk3iYPE7FE+2N27cKF4nHjx4AFNTU5iZmYljvrRs2RIRERHicaIcLC5dulSVH4HlgNjYWHh5ecHKygqPHz8GIB2CpWvXrujRowcqV66MsLAwVRUz3xs9ejQ0NDRw5swZAGnnTcVx/ejRIzg7O0Mmk6FQoUI/1dMlN/uZLmjpAxHGmNS3gsV9+/ahbNmyEAQB8+bNw82bN3H58mWMGDECgiAgODhYhaXOe2JjY8Vr8LZt26Crq4s5c+bg48ePANLGkDUyMoKdnZ1kjEWW8/bv3w99fX1YW1uja9euWLp0Kdq1ayeGwEBaPqNoscgzo2fEoWI2S0hIwLp162BjYwMfHx98+vTpu9srbgajoqIQHh4uTtYCcNeOnJTZd/2tQFe54tunTx9UrFgRnp6eYheD7702P0pISMCKFSvg4OCARYsWISIiAkZGRqhTpw5ev34NIG0SnN9//x2ampqYOXOmeAEGpN8ld31WT61bt4a1tTVmzZqFO3fu4O+//4a9vT0qVKiAo0ePSrZV3CwOHz6cZ6H9hsuXL6Nhw4YwMDBAcHAwEhMT0b17d/j4+ODSpUt49uwZVq5cCTMzM9SpUwcPHz6UBIt16tSBiYmJ5JzE8qZNmzbB2toaNjY2kpbely9fhoeHB7Zt26bC0jEAuHbtGkqWLAljY2Oxq7rCzp070bBhQ0RFReWrWUB/pAtamTJlUKNGDckkVIyxzIec+V6wWK5cObHVeq1ateDu7i4ZvollvT/++ANly5bFq1evxGW///47mjRpgrp160r2EVONW7duoW7dujAxMYEgCLC1tcW8efMk25w5cwbNmzfHvXv3VFRK9cWhYjZRDjuuXr2KcePGQRAEtGnT5l+DxZ8JtFjWU953t2/fxpEjR/Do0SN8+PBBXJ5+fyi/JiEhAbGxseLvHAanUf7+rl69Cm9vbxgYGEBPTw9NmzZFWFiYpGL0+vVrdO7cGVpaWpg1a9Y3g0WmXgIDA+Hm5ob169eLs5Veu3YNmpqaMDU1Rbly5XD8+HHJa3r16sVddP/FzZs30bBhQ+jp6WHXrl1o3769pCV0QkIC9u3bB3Nzc3HCKMVxcvnyZTRu3DjTWSFZ7qE4P8bFxWVYp3xODAwMROHChaGrq4vmzZujXbt2KFWqFCwtLfnGJQf8yDV/w4YNcHZ2hpGREZYuXYqLFy9i165d8PHxQZkyZSR1iPziR7qgKd+QM8akjh07Jvn9W8Hinj17UKNGDejr62foxcD3LNmjffv2KFKkiNgr58qVK6hatSp27typ4pIxZV++fMG7d+9w7949yZi9yvf5+WVYkp/FoWI2UD4ht2rVCuXKlYObmxsMDAwgCAIaNGiQb7q05DbKoVb79u3FboXa2tpo1qzZN8fzAaQ3dYqfOfxK8/vvv6NMmTJ48eKFuEwxdpSmpiYGDx4sLldMIABIg8U5c+YgKioqR8vNfk5qaiqmTJkCd3d3sdXphQsXoKenh99++w2LFy+Gjo4O3N3dJd3f2bcpn0Nu3ryJunXrQiaTwczMTLzxVmwjl8uxd+/eTINF5THKWO6jPLt348aNJb0YFNLP5tm/f384ODjAxcUF3t7euHv3bo6VN79S7IOwsLBMWzIoH8+7du1C/fr1xSEi9PT0YGNjg9u3b+dYedXN97qgbdiwQdyO61aMSY0cORJGRkZYvXq1ZPm3gsWQkBBUqFABOjo62LVrFwDweIrZ6PLlyxAEAa1bt8agQYPg6ekJa2trftibC/A9/Y/hUDEbde/eHQULFkRQUBBev36N8PBwjB07FgYGBvDx8eFgUQ186wTRqlUrWFtbY8aMGbhw4QKWLVsGExMT2NracpPnnySXy7Fy5UrY2tpKxj+cP38+GjRogMqVK8PAwEDS9UL5idDr168REBAAQRCwaNEifoqqRjLrcvP8+XOx4vr48WNYWlqiZcuWYsjYq1cv6Ovro2zZsuJgx+zbFN/x169fkZycjPv376NNmzYQBAEDBgzI0GVcLpdj3759sLGxQfny5cXwiStDuZdi30VHR6Ny5crw8PDI0G02/bYKr169QlxcnNhqmGW/8PBw8SZeuYW9gvI1LC4uDnv37sWCBQuwevVqScuI/OpHuqAxxqSOHz+OatWqoWjRot8NFpVDLEVXaF1dXZ70MAecOnUKjo6OsLS0RKVKlXDnzh1VF4mxLMOhYjaJi4tD8eLF4efnJ5n9Nzo6GitXroS+vj4aNWr0r12hWfZLPy7fiRMn4OjoiGXLloldkG7cuAGZTIa+ffty95v/IDk5WWxlGBERIXaFlsvluHLlCurUqQMDAwPMmDFDfI3yzfHHjx/RrVs3hIaG5mzB2Q9ZsmSJOPFDamqqOPHApEmTULRoUVy7dk0cG2vgwIGwt7eHvb09Nm7cqMpiqz1FoBgWFgY/Pz+0atUKsbGxuHr1Kvz8/KCrq4t169ZlCJLkcjl27doFFxcXDilyOcW+/fDhAw4dOoSyZctKxpz70dez7KU4VhMTE7Fw4ULUrVsXZ8+e/eb2vF++71td0PihImPS84fyMXHu3DlUqVIFrq6umQaL2tra6NSpk+QB//79+1G5cmUIgoD9+/dnf+HzuaioKLx48YJ7XrE8h0PFbPL27Vvo6emhf//+ANJCFcVF4NOnTxgwYAAEQUC7du04WFSBoKAguLu7i78rB4uBgYHQ09MTW/gcPXoUenp6aN++vaT7rvKsxOzHfP78GU5OTvD09JR8l5cvXxbHWFQOFh88eIBFixapoqjsB61btw6CIKBLly4ZumR2794d9vb24g33y5cv0bp1a6xduzZfTULwXyiuF6GhobCwsEDt2rUxaNAgcf2NGzfQoEEDFChQABs2bMg0WFR+oMVyr/fv38Pc3ByVK1dGuXLlxOWZtRRmqvPgwQMMHToUDRs2RI8ePVRdnDyHg1jGpCGiouGD8rVAESy6ublhzZo1ktcuXboUgiDgyJEjkuUhISGoWbMmP7hnjP1nHCpmo0aNGsHJyUlsaq5owQMAd+/ehbGxMQRBQMWKFSXjyLHslZycjAULFkBbWxteXl7icsU+WL58Oezs7ACktVosUKAAOnbsKGmhuHr1aowbN45nq/0ByjcCiYmJCA4OhqmpKfz8/CTBkqLFop6eHsaPH4/Tp0+jcePG0NLSQkREhCqKzn7Q8OHDYWZmhoCAADFYTElJwfjx42FmZoZJkybh+PHjGD58OPT19cXxewC+UfyeDx8+oFKlSvD09MTVq1czrP+3YJHlbsr7c+jQoRAEIdMbQqYeFi1aJI7BPHbsWHE5H5eMsaz2+++/o3v37mKLN+Vg8ezZsyhTpgwcHBywfv16yeuUu9wqB5T8EJIx9is4VPxF32spsGDBAmhqaqJLly5iqyzFCfz48eNwdXXF3LlzERgYmCNlZf/bX7GxsVixYgWMjIxQrVo1yTYPHjyAIAjw9/eHoaEh/P39Ja3qwsLCUL58efTs2ZNDxX+h/H0rWoMmJSVh69atMDQ0zBAsXr16FU2aNIEgCDAyMoK1tTVu3rypkrKzjNKf75Qfhvz5558oWLAgAgIC8PjxYwBpoVi9evVQsGBBaGtrw9jYGDNnzszRMudmN2/ehIGBARYuXCguS01NlYQUimDRyMgIf//9tyqKybKYop6gPA6iXC7HzJkzxYHelbuvMdXIrP6naAlUqFAhXLp0SQWlYozlRenPN40bN4a5uTmGDRuWabC4f/9+CIIAV1dXrFixIsP7Ka4z/NCDMZYVOFT8BcpdZtesWYPhw4dj3LhxkhnqfvvtNwiCgE6dOonNyiMiIjBq1Ch4e3vj5cuX4rZ8Ys9emzdvRnBwsNhdIC4uDsuWLcsQLH758gX9+vWDvr4+SpYsKQlOXrx4gTFjxsDGxgYhISE5/hlyE+Xx4Jo3bw4/Pz/xJjk5OfmbweLjx4+xY8cOzJs3D+Hh4SopO0ujGAcxPeXZSZW3UQ4WFS20o6OjsW3bNqxbt04y4zOPjfXvNm/eDEEQxGuH8net/PP9+/dRuXJl2NjYICYmJsfLybLeixcv4ObmhpUrV0qWjxs3DoIgoGfPnmJ4z1Tn0aNHGR4ML1y4EIIgoE2bNjyxG2PslymHhUeOHEFkZCQAoFOnTjA3N8eQIUPEYFG5blC5cmUUKVIE1tbWfC5ijGUrDhX/I+Ub4kaNGsHIyAi2trawsrKCTCZDhw4dEBcXBwDo0qULChQoADMzM9SrVw/u7u7Q0NDg2exyUGJiIkqVKiW5QQfSAsS//vorQ7B46dIlNG/eXLx5279/P7Zs2QJ/f39oaWlh9uzZqvgYuUZm48ENHjxYss33gkWmesHBwahatao4S71in3bo0AFOTk44evSouK1yJfaPP/4QZxlUzAKdHgeKP+bmzZvQ0NDAuHHjxGXpHz5NmzYNMTExePDgAU/KkodcuHABFStWhI2NDYKDgyXrxowZw8GiGkhISECHDh0gCAL++usvybrZs2eLPR6U6xz88Jgx9jOUA0V/f384Ojpi2LBh4rKOHTvC3NwcQ4cOxfv378Xld+/eRYUKFbB06VJs3bo1R8vMGMt/OFT8RX369IGtrS3WrVuH+Ph4fPnyRWydGBQUJG63du1aBAQEwNXVFQ0aNMCqVavEdVzJzBnv3r2T7JOvX78CSAsWFS0Wq1SpIq6/fv06hg4dCn19fWhqakJTUxOlS5fG4sWLxW04HPm2b40Hp1xBUg4WW7RogadPn6qgpCy9lJQUrFmzBgUKFED9+vXFYBEAzp8/DwMDA1SqVOmbwWKNGjVgaWkJf3//bwaL7N+9ffsWJUuWRJEiRXDw4MEM60+cOAELCwueRTuPOnv2LHx8fGBhYfHNYLFPnz7izOss5124cAHNmzeHqakplixZIlk3a9YsMVi8f/++ikrIGMsLGjduDHt7e8yaNSvDA8SOHTvCwsICXbp0EWcXnjBhAkqUKIEPHz6I2/E9C2Msu3Co+As+f/6MsmXLYuDAgeIMzmfPnoWBgQG6dOmCZ8+eZTiBx8XFSZbxCV41fvvtN6xdu1ZsTfqtYDExMREvXrzAgQMHcPHiRUlrOt533/et8eDSS05OxrZt2yAIAjp06CAZVoCpTlxcHDZu3IiCBQuibt26kmDx6tWr0NPTQ4UKFTINFjt06ABLS0toaGjg8OHDOV72vOTChQvQ19dHhQoVsGXLFnH52bNn0bBhQxQvXpwnMsqFRo4cKd4Yph8rSzmgP3PmDOrWrZtpsDh+/HgIgoA//vjjm0MVsF+jfGwpX7+Ur1NXrlxBkyZNMg0WZ8+eDU1NTfj5+XH4yxj7T+bOnQtbW1ts3rxZMsSJ8nloxIgRMDExgY6ODmxtbSEIAmbNmqWK4jLG8iEOFX9C+hBJMaHHP//8AwA4evQo9PT00KFDB3G8CwA4ffp0pu/HLRSzT/rvVvmG68uXL+IYI1u3bs00WKxatepPvT/L6HvjwaW/iU5ISMCuXbt48gE1k5CQgHXr1sHGxgY+Pj74+PGjuO7KlStisKgcHL548QJ+fn44depUpjMWs5938OBBGBsbQ09PD9WqVUPt2rVRqFAhWFhYSMa3ZLlD06ZNoaOjg/Pnz4vLHj9+jMGDB4vXo8yCRTMzM2zatEnyXlOnTuWxsrJJxYoV0bRpU3z58kW85r9+/Vp8wPKtYDF9V+iJEyfCxMREUi9kjLEf1aNHDxQtWlQcN/H169cIDg6Gr68vqlevjgkTJgBIuw/9888/0b17d8m1gu9ZGGPZjUPFH6QcKCoqhlFRUShSpAhmzJiBQ4cOiYHiq1evxG13794Ne3t7nsFWBRITE/HmzRvJMkUAHBUVBS8vL1haWn4zWPT09BRfx60Sf96PjAc3adIkSQs4ph6Ub5avXr0qTg7Rpk2bDMGikZERypQpg4ULF+LSpUviZC3KD1P4+Pl1YWFh6Nu3LypVqoRKlSqhb9++3LU8F9q9ezcKFiyI9evXZ5g9XUtLC3369EF8fDwAabB49OhRmJmZQU9PD5s3b87xcuc3/fv3h52dHUJCQsShUt6/f49ixYrBz89P7J2ifK68fPkyatasiQIFCmD16tWS91OEAYwx9qNSU1ORkpKCgIAAFC5cGNu3b8fx48dRqVIlWFtbo1SpUihfvjw0NTUl4/Qrn5e4/sUYywkcKv6kpk2bYuDAgeLvjRs3homJCfT09NC+fXvJ2BXPnj2Dv78/atasyQPo5zC5XI6tW7eiXr16uHTpEgDA29sbDg4O4rh9Hz58EMd+27JliyRYXL58OXR1dVGqVClVfYRc70fGg7OyspKMc8lUT7kC2qpVK5QrVw5ubm4wMDCAIAho0KCBeEMNpIXHHh4e0NPTg6amJrS0tDBjxgwVlDzvUw7lueVB7rR+/XpoaWlh586dANJ6PAQFBSExMRG9evWCnZ0devTokWmwOGzYMFhaWkImk2VosciyTmJiInx9fdGwYUPxIcrbt28BAL169YKTkxP8/f0zDRb37NkDQRBgamrKE7oxxrJEaGgozM3NoaurC0EQUKtWLSxfvlxc7+rqilatWqmwhIyx/E6T2A/79OkTPX/+nAwNDcVlK1eupEaNGtGdO3eofv36VKBAASIievLkCQUHB9Pu3btp4cKF5OjoqKpi50saGhqkra1NoaGh1Lp1a7KysqKXL1/S0qVLydramoiIzMzMKCQkhJo1a0b9+/cnQRCoUaNGpK+vTx07dqSvX7+Sjo6Oij9J7mVpaUmBgYFUt25dGjNmDMXExFCbNm2IiOjcuXM0e/ZsMjU1JW9vbxWXlBERASBBEEhDQ4OIiHr06EHHjh2jBQsWkK+vL339+pXWrFlD8+fPpzZt2tDWrVvJxMSEypQpQ/v27aMbN25QZGQkOTs7k4+PDxERpaamiu/HGCOqUaMGGRgY0Pr16wkA/fbbb9SoUSPq0KEDzZkzh5KTk2nfvn0kCALNnz+fChQoIB6bz58/p5IlS5K5uTmVK1dO1R8lTwJAGhoalJCQQJGRkZScnEy3bt2iJk2a0LJly2jJkiU0ePBg2rFjB/Xv358WLVpEBQsWpMTERNLR0SEfHx9yc3MjPT09Wr58OXXt2pVMTExIEARVfzTGWC5VvHhxunLlCl2+fJkMDAyoVq1apKenR0REL1++JENDQ/HeRnG9YIyxHKXSSFPNZdYS5Pfff4eLiwtSUlLE9adPn4arqyuMjY1Rv3599OvXD5UrV4a+vj6mT5/+3fdj2WvTpk1iC6qpU6eKy5VbYym3WNy2bRu+fPkCQNr6gPfdf8fjwamvAwcOYN++fRmWx8XFoXjx4vDz8xOPBwCIjo7GypUroa+vj0aNGkm6QqfHXW4YS3Pjxg3J78ePH4eenh50dXVRvXp13L17V1z35csXdO3aFXZ2dggICBC7SN+6dQt169bFvn37kJCQkJPFz5euXLkCCwsLVKhQAQUKFEC9evXEMX+Tk5MxYMAA2NnZoVOnTpIeKnv27EGNGjVw9OhRvHz5UlXFZ4zlA0+fPsWkSZNgZGQktn5njDFV4CYk6aSmphIAksvl4pOe5ORkcX2pUqUoKiqKoqKixPWenp507tw58vPzo7dv39L27dvJycmJVq1aRSNGjBDfl58c5Ry5XE5ERBEREWRoaEjm5ua0fPlyOn/+PBERCYJAAIjofy0WixcvTt27d6ddu3ZRSkoKaWr+ryEv77v/rl69enT58mUKCAiglJQUiouLo4YNG9K5c+eodOnSqi5evgSAXrx4QU2bNqUhQ4bQs2fPxHWpqan05csXev78OTk6OpK+vj6lpKQQADI2NqbWrVtT165daf/+/dSnTx/69OmT+J7KuIUiY0SdOnWihg0b0qFDh8RlZmZmlJycTMnJySSTycTrCwDS19enhQsXUvPmzenAgQNUvnx5CggIIH9/f7px4wYVK1aMW9Bng379+tHmzZvF3ytUqECjRo2ia9eukYGBAXXr1o2KFi1KRESampo0d+5catmyJZ06dYratWtH9+/fpz179tDy5cuJiKh69epkZ2enks/CGMv7NmzYQJMnT6bp06fT6NGjqXnz5qouEmMsHxOQ/k4wnwoKCiJDQ0Nq0aKFZHnnzp1JLpdThQoVyN3dnV6+fEk9evSgXbt2UcOGDTO8j1wup9jYWDIxMRGXcRdA1fn48SO9fv2aLl26RNOnT6ekpCQKDg6mmjVrikGv4obu48eP5OnpSQMHDqQePXqouOR5D5S6ZIC7Z6iFTZs20bt372jgwIEZ1jVu3Jju3r1LR48epSJFihAASk1NJZlMRvfu3aPq1avT58+fqUKFCnT27FnS1tZWwSdgTL3dvHmTvL29ydnZmaZOnUoNGjSgwMBAOnv2LBUtWpSmTp1KtWrVohkzZlCpUqXE1yUkJNDatWtp+/btFBYWRk5OTrR8+XLJNixrfPjwgSwtLcnNzY1mzpxJfn5+lJCQQDVq1KCUlBR6+vQpeXh40KJFi6hUqVJifU4ul9OMGTMoMDCQnj9/TjKZjExMTOjYsWPk7u6u4k/FGMurnj9/TmXLliV7e3saMGAAde/enYj4fpMxpjocKlJahbJRo0Z05coVevjwIRUuXJg0NDTo8uXLNHz4cIqOjqb79+9TamoqEaUFIhUqVKA6depQmTJlqHLlymRsbEzGxsYq/iT5m1wuJ5lMJv6evrVhUFAQTZ48WQwWa9WqRURE0dHR4k1DUlIShyPZhENF9ZHZ9z969GiqWrUqNW7cmIiIFi5cSMOGDaNOnTrRxIkTyd7eXqywnjhxgnr27Em9evUiY2Nj6tq1qyo+BmNqTXENUoTwTk5OtHjxYvLy8qKvX7+SIAi0Y8cO6tmzJ9WsWZNmzZpFJUuWzPA+r1+/JkNDQzIwMFDBp8jbFOe0iIgIqlq1KhkYGNCsWbPIz8+PTpw4QVZWVhQWFkZdu3al0qVL04IFC8jDw0N8PQB6+vQpnT17luRyOXl7e5OTk5MKPxFjLD94+vQpJSYmUrFixYiIA0XGmGrl+1Dx77//JhcXF9LQ0KDIyEhq3769uE45pHr16hW9evWKTp8+TSNHjiQ7OztKSEig9+/fk1wuJzMzM3Jzc6O1a9eSq6urqj5OvqW8r1asWEEPHz4kANSuXTuqVKmSuF1wcLAYLG7ZsoXc3d1p6dKlNG7cODp79iyVK1dO7BrNoRfLLy5dukRVq1YlLy8vGjVqFPn6+hJRWtfNDRs2kL+/P40cOZKKFy9OL168oOXLl9PFixcpODhY7OLHxwxjGSmuTXfv3qUaNWqQg4MDzZ07VzzG4uPjaceOHdS7d+8MwSLfJOYMxT6KiIigypUrk56eHi1evFjsjZKSkkK7d++m7t27k7u7O82fP18SLDLGmCpx/Ysxpmr5OlQMDAykHj160Jw5c2jQoEFi5b158+bUoUMHat26NQGglJQU0tLSEl9Xq1YtAkBbt26l58+f0+3bt+nhw4fk6OhI/fr1U9XHybeUb7yaNGlCZ86cIV1dXZLJZPT69WtauHAhderUSeySHhwcTFOmTKHw8HDy8PCgGzdu0Lhx42jcuHEq/BSM5ZzMwoqQkBDq27cvubi40MiRI6lBgwZERBQQEEBbtmwhPT09qlChAr1+/Zru3r1Lc+bMoT/++EMVxWcsV1Dc6Cn+HxoaStWqVSMnJyeaMWOGeIwlJCTQ9u3bqVevXuTl5UVz5syhEiVKqLj0+UtycjJpaWmJwaKhoSHNmjWLmjVrRkRESUlJtHfvXurWrVuGYJFv6BljjDGWn+XbUPHNmzdUq1YtqlGjBk2bNo0sLS2JKK1FYv369enevXu0Z88eatiwoXgDrqh0dunShS5evEihoaGZvjdXMFWja9eudPjwYZo0aRI1btyYAFDPnj1p3759NG3aNOrevbvYRX3fvn20d+9eevr0qTjxBBG3DGF5n3Kr3tu3b0vGCAsJCaHevXuTq6urJFhcvXo1nTt3js6cOUNFihShFi1aULdu3YiIz3eMZUZxXLx7906sXxDRvwaL/fv3pxIlStDff/8tdmtj2Sf9sClE9EPBYrly5Wj69OlUsWJFFZSaMcYYY0yNZOvc0mrs+fPnsLCwQJ8+fcRlM2fOxNevX3Ht2jXUq1cPgiBg7969AIDU1FTI5XIAQHBwMLS0tHDv3j2VlD0/CwoKwqFDhzIsP3LkCEqXLo25c+ciOjoaAHDixAkYGhqiUKFC0NDQwPTp0/Hp0yfJ675+/Sr+rNi/jOVVKSkp4s9du3aFg4MDFixYIPnb37VrF6ytreHp6Yn9+/dLXh8XFyfZlo8Zxr7t7du3KFKkCDp16iRZfu/ePRgbG8Pd3V1yjH39+hWBgYGws7NDREREThc333r06JGkLgik1RGtra3h6uqKXbt2icsTExOxa9cuCIKARo0aISEhIYdLyxhjjDGmXvJtkyxHR0cqX748/fPPP3TkyBGqXbs2LV26lMLCwqhcuXI0ceJEqlu3LjVp0oT27dsnaYlTqFAhSklJoaioKBV+gvwnKiqKli5dSk2aNKGTJ09K1sXExJCVlRW1atWKjI2N6dq1a9SoUSNq2rQp7dmzh1q1akVjx46l4OBgio6OFl+nq6tLRGmtSriFIsvLAIgtcpo0aUKHDh2irl27UoMGDUhDQ0OciKpZs2a0bNkyevToEU2fPp0OHz4svkeBAgXE44SPGca+LzU1lapWrUqHDx+mPn36iMtLlChB58+fp+fPn9OIESPowIEDRJR2PfL396fQ0FBycHBQVbHzldTUVAoKCqJly5ZRly5dxOWOjo506dIlio2NpT///JNCQkKIiEhbW5saNGhA//zzD82dO5d0dHRUVHLGGGOMMTWh4lBTJVJTUwEA7969Q7ly5aCvrw8TExNs375d0pLn4sWL8PHxkbRYVLxOQ0MDO3fuzPGy53dXrlyBj48PjIyMcOzYMcm6p0+fAgBevnwJV1dX+Pn5ia09goODIQgCBEHAmDFjkJycnNNFZ0wtjBkzBnZ2dti2bRs+f/78ze127NgBe3t7VKlSBfv27cvBEjKW+yla8UZGRqJ3794wNTVF7969JdsoWiyWK1cOISEhqigmQ1qdbtiwYTA3N8/QqjQiIgI2NjZwdXVFSEiIWH9kjDHGGGNp8mUzE0WrQwsLC9LW1qb4+HjS1tYmmUwmGVuncuXKNHnyZLHF4v79+4korbXOvn37qHnz5iopf36E/x/6s0KFCjRjxgyqUKECNW/enI4fPy5u4+zsTEREd+/epejoaPr999/F1h4FChSg6tWrU8+ePcnc3Jw0NTVz/DMwpg6uX79OxYoVI29vbzI0NKT379/Tvn37yN/fn3r06EEbN24kuVxOLVq0oIULF9KNGzcoLi5O1cVmTK3J5XIiSht3TyE1NZVsbW1pzJgx1LZtW9qyZUumLRZv3LhBc+bM4eMsByhaZCukpKSQhYUFDR8+nDp37kwHDhygzp07i+sdHBzo0qVLlJiYSF27dhXrgYwxxhhjLE2+TlZevXpFtra2NH/+fFq9ejUNHDiQZDIZ1atXj7S1tYnof8GiTCajxo0b09atW6lVq1ZUv359IuKJPXKKcvfzcuXK0ezZs+nPP/+k5s2b086dO6lOnTri+vDwcPrw4QO5uLgQEdG7d+/oyJEjZGNjQ7NnzyYDA4McLz9j6iAxMZFevnxJpqam9PnzZ3r27BkNHz6c7t69SzKZjBISEuiff/4hKysrqlOnDrVo0YJCQ0OpcOHCqi46Y2pNJpPR06dP6Y8//qBx48ZRuXLlKDU1VRIsAqB169aRjo4OzZ8/n4jSgsXQ0FASBIH09fVV/CnyPg0NDYqIiKB3795RhQoVSFNTk+RyOZmZmdGoUaOIiCgoKIh+++03CgoKIqK0YPHUqVPUoEEDnjyHMcYYYyydfDv7s8LXr19JU1OTwsLCyN/fnz5+/Eh//fUX+fr6isEiEdGFCxfojz/+oA4dOtCAAQNUWOL8DUozzV6/fp3+/PNPunLliiRYjIyMpGrVqpGjoyM1adKEHjx4QBs3bqSlS5eKszwzltd964FHSEgItWzZkoyNjSk6OpoqVKhArVu3pmHDhtHHjx/JwsKCRo4cSVOmTCGi/x1z/ACFse9bsWIFDR8+nCpWrEhz584ld3d3sWWchoYGvXjxglq2bElXr16lQYMG0bx581Rc4vzn8+fPVLduXfry5QutXr2aqlSpQkT/mwX6w4cPNGrUKAoMDKQePXrQ8uXLxdempKRwLwfGGGOMsXTyfaioAIBu3rxJXbp0oaioqEyDxbdv35KVlZUKS5k/KSr7mbl69SqNGDFCEiwmJSXRwYMHadSoUXT//n2ytbWlwYMH0x9//EFE0mCSsbxI+eY3MjKS3r9/T0WKFCFNTU3S1dWl06dP0969e6l48eJUv359srGxISKiR48eUa1atWjIkCE0ePBgVX4ExtSeImhXvqYsXryY5s+fT87OzrRgwQIxWJTL5aSlpUWbN2+mP//8k169ekUDBw6kuXPnqvhT5D+LFy+mZcuWkYmJCc2dO5eqVq1KRP+ra7x48YLKlStH8fHx5OPjI07SwnUHxhhjjLGMOFRM58aNG2KwuHz5cqpbt64kWCTiimVOUg5HLl26RC9fviRbW1syMzMjNzc3IkoLFocPH05Xr16lHTt2UN26dYkobWyrR48ekYGBATk5ORERd1dneZ9yCB8QEEDHjx+nFy9ekI2NDbVo0YIGDBhArq6uGY6Fly9f0po1a2jRokW0ceNG8vHxUdVHYEztKY6zly9f0rlz58jR0VEMpxYtWkQLFiyQBIsKQ4YMoatXr1KlSpWoe/fu4nWMZQ/FfoqNjaXY2FiytbUlIqLVq1fTjBkzyNzcXAwWlc+dZcuWJWNjY3rz5g2dOHFCfB1jjDHGGJPidCUdDw8PWr16NVlaWlKXLl1o3759GQb25kAxZ6SmpoqBYrt27ahBgwbUunVrql69OtWrV4/+/vtvIkqbvGXmzJlUsWJFatGihTh5i7a2NpUsWVIMFAFwoMjyPMVNcfPmzengwYPUvn172r17NzVr1oz+/vtvat++Pb19+1ZyLOzbt49mzZpFU6ZMoT///JMDRca+QxE+3b9/n3x8fGj69Ol0+/ZtcUKxAQMG0KBBg+jZs2fUt29funr1KhGlPbQMDQ2ltm3b0syZMzlQzGaK/fTw4UNq3749TZgwQdwXXbp0oREjRtCHDx9o6NChdObMGfHcee7cOTI2NqbZs2fTuXPnOFBkjDHGGPsOHhwmEx4eHrRy5Upq1aoVffr0iYMoFVF87x07dqSTJ0/SiBEjqFatWnTjxg0KDAyk7t2708ePH2nYsGHirNCjR4+mhg0bUkhIiDiZjgKHwSy/CAkJoStXrtC0adOodevWZGBgQObm5rRs2TKqWrWqeGwBoHPnzlG3bt1IX1+f5s6dS/369SMibtXL2LfIZDJ69OgR1axZk8qXL0/du3enFi1aSLYZMGAAaWpq0rJly6hmzZpUpkwZioyMpC9fvtD8+fP52MpmqampJJPJKDQ0lOrUqUOOjo7k4+NDFSpUELfp0qULpaam0sKFC+n333+nsWPHEgDatGkTvXnzhtzc3MjExER1H4IxxhhjLBfg7s/f8eHDBzI3N1d1MfK1e/fukbe3N3Xv3p1GjRpFenp6RER07do1mjZtGu3atYuCgoKoU6dO4vLevXtTQEAA9e7dW5VFZyzHpA8AJ0+eTEuXLqU7d+6QhYUFHT16lJo1a0Z+fn40Y8YMcnBwIACUlJREcrmcLl26REZGRlS+fPlM348x9j8JCQnUtWtXevjwIf31119UsWJFIkoL6WNiYujz58/k6OhIREQHDx6kkydP0smTJ8nBwYEmTpxIJUqUUGXx8403b95QnTp1yNbWlqZNmybuJyLpMDbbtm2jVatW0dGjR0kmk5GVlRXt379f0m2dMcYYY4xljkPFH8BjKOac9N/1sWPHyMfHh44ePUre3t6UlJQkjnF5+fJlCggIIC0tLTp48CBZW1sTEdH79+/JwsJCJeVnLKcpuvgBoLNnz5KnpyeNGzeOQkJC6Pbt23Ts2DFq2rQpNW/enGbPni1OyrJmzRpKTEykHj16SAJEPt8x9n0pKSlUpUoVcnBwoF27dhER0alTp+jQoUO0YcMG0tPTo2bNmtH06dPF18THx5NMJiMdHR1VFTvf2bNnD3Xp0oVWrVpFzZo1IyKiZ8+eUVhYGF25coVsbGyoa9euRET08eNHCg8Pp48fP1Lp0qXF8yRjjDHGGPs+7v78A/gGO2cot45SBCUFCxYkQRDo8OHD5OXlRdra2uJ2lSpVos6dO9PIkSMpNjZWDBUVgSKHIyw/UIwD5ufnRw8fPqSjR49S1apVacqUKTRx4kSaO3cuNWvWjGbOnCneKN+9e5dmzJhBrVq1IrlcLgkV+Zhh7NsAUFxcHFlaWtLz589p48aN9PjxY1q1ahUBoBo1atCbN29o9uzZVLJkSfL39yciElvZs5yTlJRE8fHxlJiYSEREQUFBtHr1ajpz5gxpa2tTUlIS3bx5kxYvXkympqZkamqq4hIzxhhjjOU+3L+NqQ1FsNGiRQv666+/iIioaNGiVKpUKdq5cydduHBBnGwlJSWFiEgMEr9+/Zrh/TgcYXmZ4hggIjp69CjduXOHxo4dS4aGhuTl5UWNGzemSZMmUdmyZSk4OJjs7OyIiCgyMpI2b95MX79+JS8vL9LS0lLVR2As1xEEgYyNjal379706dMnCggIoEmTJlGrVq1ow4YNtHnzZlq2bBnp6+tTRESEqoub532vs03RokXJycmJBg0aRG5ubuLYsZs3b6bnz59T27Zt6Z9//qEnT57kYIkZY4wxxvIWbqnI1MqHDx/ozZs3tGbNGmratCk5OTlRYGAg1a9fn0aNGkWTJ08mLy8v0tTUpDdv3tDZs2fJ2dmZjI2NVV10xnKUYmb0KVOmkIWFBZUsWZJat24tDg/Qq1cvev/+PZ0/f57++usvqlmzJr179462bt1Ka9eupZkzZ1K9evVU+REYy7WaNGlCrq6u9OHDB7K3tyd7e3vxmHzz5g0VLFiQrKysiIhbzWeH8PBwKly4MAmC8M3vt1SpUrRs2TLavn07RURE0IwZM6hcuXLk7OxMRET29vZiSMwYY4wxxv4bDhWZWjE3N6cOHTrQ0KFD6caNG+Tk5EQeHh7ibM+dOnWievXqUdmyZen06dO0a9cumjNnDjk5Oam66IzluH379tG4ceOIiMjX15e0tbXFoQMaNmxIWlpatHz5cho4cKAYeDg4ONDs2bNp4MCBRMSTsjD2sxQhVrFixTKsO3fuHM2ZM4c0NTXJ19eXiLjVfFbr1KkT3bx5k9auXUvly5f/brBYs2ZN8vT0JADiUBFERNevX6crV65Q6dKleZxLxhhjjLFfwBO1MJVJH2YowhAioqpVq1JycjJdvnxZ3ObOnTvUp08funv3Ln358oWKFi1KPXv2pP79+xMRtwZh+U90dDStXbuWlixZQl+/fqWjR49S8eLFKSUlRQwRExMT6fr16/Tw4UOysrIiJycnKl68OBFxoMhYVvny5QvNnDmTjh07Rk+fPqXDhw9T6dKlVV2sPOnQoUPUvn178vDwoFmzZomz1v9oHWDXrl20ZMkSun37Np06dYpn42aMMcYY+wUcKjKVUK78JyYmii0FFLM7r1q1ioYNG0YzZsygXr16ieFHUlISvXv3juLj48nIyEgcU5HDEZZfxcTE0Jo1a2jChAlUokQJOnjwIBkZGUlC+sxwCM9YRt87br637tChQ+Tv7081atSgGTNmUNGiRbOzmPneqVOnqFmzZlS2bFmaM2fODwWLKSkp5OfnRzdv3iRzc3PasGEDlSpVKieLzRhjjDGW53CoyFSqVatW9OXLF+rYsSN16tRJXP7u3Tvy8vIiR0dHOnz4MBHR/7V377FV1wcfx99tD5dK0QEbtQiYzgLLYgkolkqBFKGSbE7ACU4msjXMSc0uTKATKUNavA2DHStjE3GoyWAG7FBAqoSTsTkN0k4U2sh1tFwCA1fKoUJPz3n+2Ho2nu3ZrM/KseX9+qtJv20+356cpv38vpeLVl/9I8sRXe7OnDnDc889x/z58xk6dCibNm0iJSXF94bUCi2l4YEDB3jmmWd4//336d+/P1lZWUyfPv2iMf/KwYMH6dmzp2f0XSLBYJBJkya1qljcsmUL1dXV3HnnnfTt2/dSxpUkSeqQLBUVN42NjSxbtoyysjJOnz7NDTfcwNy5cxkyZAjXXHMNL730EnfddRfPPfdc7B86Sf9aQ0MDq1atsliUPoGW98mePXvIzc0lOTmZq6++muPHj9PY2MioUaNYt27dv/1aXXrBYJCJEycydOjQj10surNBkiTpv8dSUXF37Ngx1q9fz8qVK6mpqWHw4MEsXLiQ1NRUZs2aRbdu3Vi5ciV9+vSJd1TpU62lWFy4cCFf+MIXqKiooHv37vGOJbULp06dYty4cfTq1YuSkhKys7M5e/YsWVlZfPDBB2zbto1Ro0bFO6b+pqU4/CTFoiRJkv47fFSruEtLS+OBBx6gqqqKhQsX0qlTJ77yla/w1FNPEQqF2L59O/v27Yt3TOlTr3v37uTn51NUVMTbb7/Nli1b4h1JajcOHTrEiRMnmDFjBtnZ2QD8+Mc/5sCBA6xcuZIhQ4ZcNN5nspdOc3Nz7OOWn3tLYZibm0t5eTlVVVXMnj2bnTt3XvR5SZIktR1LRX0qtPyTUFhYyMsvvxw7z6q2tpZQKMShQ4fiG1BqJ7p3786MGTOorKzkzjvvjHccqd2oq6vj5MmTDB8+HIC5c+fy2GOPsWzZMqZMmUL37t05d+4c5eXlgKXVpfKPZ10WFhby5S9/mdmzZ1/00OT/KhYlSZLUttz+rE+N/71V6cCBA7zzzjs0Nzdz9913xzGZ1H55fpj08QSDQcaPH09FRQUVFRU8+eSTLF++nHvuuYfk5GQAZs2axb59+/jlL39Jr1694py442v5/bVnzx5uueUWkpKS6N27N7W1tfTq1Yvp06czb9682PiWy1uGDRtGcXFxbMWpJEmS2oalotqNf3frpiRJH8e/K9pHjhzJu+++S2NjI88++yyTJ0/miiuuAOAPf/gD3/3udxk2bBhLly6la9eulzL2Zauuro7x48fTt29fHn74YUaPHs3JkyfJzs4mFArx7W9/m0ceeSQ2/re//S25ubncdtttvPTSS3Tp0iWO6SVJkjq2QLwDSB+XhaIk6f+j5eHUkSNHCAaDHDhwgMzMTDIzM7nuuutYsGABhYWF1NbWMmjQoFghtW3bNp588klOnDjB7NmzLRQvgWg0SiQS4Ve/+hXNzc089NBDjB49GoCysjIOHTrE9ddfz/Lly+nUqRPz588HYPTo0WzfA2W7vQAACjVJREFUvp3PfvazFoqSJEltzFJRkiR1eJFIhKSkJHbv3s1tt91GQ0MDzc3N1NfXM3LkSAoKCvja177GyZMnKS4uZuzYsdx4442Ew2GOHDlCOBxm8+bNXHfddfGeSod2/vx5unTpEnu90tLSyMrKIjc3F4CSkhIWL17MCy+8wPDhw8nLy2Px4sVEIhEWLFgAQE5OThxnIEmSdPlw+7MkSbos1NXVMWrUKDIyMvjBD37AiBEj2LlzJ+PGjSMrK4t169aRmprK/v37WbFiBbt37yYajZKTk8O0adMsFNvI4cOHqa6uJi8vj8TERHbt2sXSpUspLS0lOTmZhIQEAoEAv/nNb5g6dSoLFy5kxowZ9OjRgyVLlvDUU0/R1NTErFmzePjhh+M9HUmSpMuGKxUlSVKH1nKO4muvvUZSUhLz5s1jzJgxAOzYsYNAIEBBQQFXXXUVgUCAQYMGsXTpUj766CO6dOniTc9tKBKJsGnTJpYsWcL06dO54447GDFiBLfccgsNDQ1ceeWVtDz/fuedd+jfvz933303PXr0AKCmpoZBgwbRr18/pkyZEs+pSJIkXXa8ElSSJHVIkUgEIFYK7t69m4SEhNjZfHPnzqWoqIiysjLuuOMOUlJSCIVC/OlPfwKga9euFoptLDExkS996Uv07duXp59+mptuuonRo0dTWlpKWloa8PfX78yZM5w+fZrm5mYAKisr2b9/P/n5+Tz//PMMGDAgbvOQJEm6HFkqSpKkDqdldeJ7773HokWLALjqqqtoamoiKSmJoqIinn76acrKyrjnnntISUkBID8/nzVr1uDpMJdGJBKhf//+FBUV8Ze//IVIJEK/fv1IT08nMTGRcDgcGzt8+HDC4TDTpk3jW9/6FjNnzmT37t1kZ2db/kqSJMWBpaIkSepwEhMTOXz4MOPHj2fHjh3s3buX22+/nfr6egYPHsxjjz3GypUrmTx5MsnJyQBs3bqVqqoq4O+rHNW2EhP/+qdoXV0deXl5DB48mHXr1lFUVARAIBCIFYtTp07lRz/6ER999BFbtmyha9eubNu2jYEDB8YtvyRJ0uXMi1okSVK7dtddd3H//fczZsyY2ArFpqYm9uzZw3333UdpaSnZ2dmEQiG+//3vs2bNGm688UaCwWDse7z99tssWLCA2tpaXnvtNfr37x+/CV0GotHoP60ujEaj1NXVce+997Jr1y5mzpxJSUkJAOFwmEDgr0eBNzQ00NDQwBVXXMFnPvOZSx1dkiRJf+NFLZIkqd2aMmUKmzZtIj8/n2g0SmJiIgcPHiQ3N5eBAweSlpZGdnY2AN26dWP+/PmcPn2a8vJyRo4cycSJE9m/fz9vvfUWtbW1BINBC8U21tzcTFJSEvX19Zw5c4ajR49yww03ANCvXz9WrVrFN7/5TX72s58BUFJSQiAQoKamhldffZV7772XPn36xHMKkiRJwu3PkiSpnaqsrCQYDLJ48WLGjh1LQkIC0WiUY8eOkZSUxFtvvcWFCxeIRCI0NTXR3NzMtddey7Jly3jiiScIhUI8+uijvP766wwaNIjf/e53XH/99fGeVofWUijW1NQwYcIEhg0bRk5ODllZWSxfvpwjR46Qnp7O6tWryczMZMWKFXzve99j69atzJkzh8cff5zGxsZ4T0OSJEm4/VmSJLVTv//97xk1ahRPPPEEc+bMoaamhp/85Cf89Kc/5dVXX2XRokVUVlby8ssvM2HCBCKRCNFolKSkJKLRKOFwmP3795OWlkbnzp1jZyuqbe3bt48RI0aQkZFBbm4u3bp1o7y8nJ07dzJt2jQWLVrEtddey+HDhykoKKCiooJAIECPHj3YuHEjQ4YMifcUJEmShKWiJElqx8aMGcOhQ4d48MEHKSwsZNy4cfziF78gNTWVDRs2MG/ePD744AM2btxIXl4ekUgkdpafNwZfWi1F7ne+8x3eeOMNXnzxxdjWdIBvfOMbvPDCCxQUFPDII4/Qs2dP/vznP7N9+3ZOnTrF2LFjSU9Pj+MMJEmS9I8sFSVJUrvTctHHhx9+yM0338zBgwfJzMzkmWeeYejQobFxGzZsYP78+Rw8eJD169eTl5f3Ly8J0aWTm5vLhQsXePPNNwG4cOECnTt3BmDy5Mls3ryZ119/nZtvvjmeMSVJkvQfeKaiJElqd1pKwQ8//JBjx47RqVMnjh49ytGjR4lEIrFxt99+O8XFxaSnpzNp0iS2bt0aO3tRbaflNWhqagKInYMYCoU4f/48586d49y5c4TDYTp37kxzczMAS5YsITExkbVr18YnuCRJkj42S0VJktRuJSQkMHPmTFavXk1qaiozZ85k48aNXLhwITZmwoQJFBcXM3DgQPLy8ggGg65UbEOHDx+mrKyMo0eP0qlTJ6qqqhg6dCjV1dV069aNr3/96+zatYtXXnmFQCBw0crRHj16kJKSQn19fZxnIUmSpP/EUlGSJLVb6enplJSU8NWvfpXVq1fTs2dPHnjgASoqKv6pWHzooYcYMWIEV199dRwTd3zHjx+ntLSUiRMnsm3bNsaOHUvPnj1JTPzrn515eXnk5OQwdepUNm7cSEJCQuxz7777LomJiXz+858HcEWpJEnSp5hnKkqSpA4hGo3yxz/+kfz8fE6dOsXy5cu59dZbY+f1AZw9e5aUlJQ4puz4Ghoa2LRpE/fffz+NjY3k5OTw85//nIyMjNiYzZs3M2/ePN57771Y2XvixAlWr17Nnj17ePPNN2PFoiRJkj6dLBUlSVKHUlVVFSsWV6xYwbhx4y4qFtX29u7dy7Bhw2hoaCAzM5OKigpSU1MJh8MEAgEAgsEgq1at4sUXXwTgyiuvpE+fPqxdu5bMzMx4xpckSdLHYKkoSZI6nKqqKu677z6qq6tZv349t956a7wjXVaOHz/O888/z/nz5yktLWXAgAGsW7eOPn360NzcTFJSEgDhcJjKykr27t1L7969yczMdHu6JElSO2GpKEmSOqQdO3bw4IMP8uyzzzJgwIB4x7ksnTt3jjVr1jBnzhwGDBhAeXn5RaVhY2MjycnJcUwoSZKkT8pSUZIkdVjnz5+nS5cu8Y5xWTt79ixr166lsLCQjIwMXnnlFT73uc+xd+9eHn30UQoKCrjpppviHVOSJEmtZKkoSZKkNhUKhVizZg0//OEP6devH5MmTWL79u288cYbvP/++3zxi1+Md0RJkiS1kqWiJEmS2lwoFGLDhg0UFxdTV1fHNddcw69//WsvZZEkSWqnLBUlSZJ0SUQiEerr66muriYjI4PevXvHO5IkSZI+IUtFSZIkSZIkSa2SGO8AkiRJkiRJktoXS0VJkiRJkiRJrWKpKEmSJEmSJKlVLBUlSZIkSZIktYqloiRJkiRJkqRWsVSUJEmSJEmS1CqWipIkSZIkSZJaxVJRkiRJkiRJUqtYKkqSJEmSJElqFUtFSZIkSZIkSa1iqShJkiRJkiSpVSwVJUmSJEmSJLXK/wDjGp5o1dRzDgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation Colormap\n",
        "plt.figure(figsize=singlePlot)\n",
        "\n",
        "plt.imshow(np.corrcoef(dataset.T), vmin=-.3, vmax=.3)\n",
        "\n",
        "plt.xticks(range(len(dataset.keys())), labels=dataset.keys(), rotation=90)\n",
        "\n",
        "plt.yticks(range(len(dataset.keys())), labels=dataset.keys())\n",
        "\n",
        "plt.title('Datset Correlation Colornap')\n",
        "\n",
        "plt.colorbar()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 829
        },
        "id": "8IhuUFy9Q1As",
        "outputId": "5d21035f-f4f4-4974-d4fc-6a86ac7facdf"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x900 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set columns to zscore\n",
        "cols2zscore = dataset.keys()\n",
        "\n",
        "cols2zscore = cols2zscore.drop('quality')\n",
        "\n",
        "# Normailize the dataset using Zscore method\n",
        "\n",
        "dataset[cols2zscore] = dataset[cols2zscore].apply(stats.zscore)\n",
        "dataset.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "DLPBwzLcRs2a",
        "outputId": "e514887f-3856-4d91-b4b8-268727a24593"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-93add6ce9303>:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  dataset[cols2zscore] = dataset[cols2zscore].apply(stats.zscore)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       fixed acidity  volatile acidity   citric acid  residual sugar  \\\n",
              "count   1.597000e+03      1.597000e+03  1.597000e+03    1.597000e+03   \n",
              "mean    3.559388e-17      1.245786e-16  1.779694e-17   -9.788316e-17   \n",
              "std     1.000313e+00      1.000313e+00  1.000313e+00    1.000313e+00   \n",
              "min    -2.136087e+00     -2.280762e+00 -1.391823e+00   -1.169149e+00   \n",
              "25%    -7.006076e-01     -7.718255e-01 -9.286769e-01   -4.525789e-01   \n",
              "50%    -2.412541e-01     -4.530063e-02 -5.384522e-02   -2.376079e-01   \n",
              "75%     5.051954e-01      6.253377e-01  7.695258e-01    4.902022e-02   \n",
              "max     4.352281e+00      5.878672e+00  3.754246e+00    9.292776e+00   \n",
              "\n",
              "          chlorides  free sulfur dioxide  total sulfur dioxide       density  \\\n",
              "count  1.597000e+03         1.597000e+03          1.597000e+03  1.597000e+03   \n",
              "mean  -1.423755e-16         8.898469e-18         -5.339081e-17 -8.361001e-14   \n",
              "std    1.000313e+00         1.000313e+00          1.000313e+00  1.000313e+00   \n",
              "min   -1.604575e+00        -1.422837e+00         -1.262581e+00 -3.546932e+00   \n",
              "25%   -3.721412e-01        -8.478700e-01         -7.596977e-01 -6.111392e-01   \n",
              "50%   -1.809014e-01        -1.770746e-01         -2.568145e-01 -6.216372e-04   \n",
              "75%    5.283609e-02         4.937208e-01          4.975104e-01  5.780428e-01   \n",
              "max    1.112349e+01         5.380944e+00          3.734821e+00  3.683719e+00   \n",
              "\n",
              "                 pH     sulphates       alcohol      quality  \n",
              "count  1.597000e+03  1.597000e+03  1.597000e+03  1597.000000  \n",
              "mean   3.025479e-16  8.898469e-17 -5.695020e-16     5.634314  \n",
              "std    1.000313e+00  1.000313e+00  1.000313e+00     0.806630  \n",
              "min   -3.709380e+00 -1.937318e+00 -1.899221e+00     3.000000  \n",
              "25%   -6.587444e-01 -6.392206e-01 -8.653154e-01     5.000000  \n",
              "50%   -9.673073e-03 -2.261897e-01 -2.073755e-01     6.000000  \n",
              "75%    5.744912e-01  4.228588e-01  6.385471e-01     6.000000  \n",
              "max    4.533826e+00  7.916418e+00  4.210221e+00     8.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c49825be-9c4c-4552-a2fd-8b3069842588\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.597000e+03</td>\n",
              "      <td>1.597000e+03</td>\n",
              "      <td>1.597000e+03</td>\n",
              "      <td>1.597000e+03</td>\n",
              "      <td>1.597000e+03</td>\n",
              "      <td>1.597000e+03</td>\n",
              "      <td>1.597000e+03</td>\n",
              "      <td>1.597000e+03</td>\n",
              "      <td>1.597000e+03</td>\n",
              "      <td>1.597000e+03</td>\n",
              "      <td>1.597000e+03</td>\n",
              "      <td>1597.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.559388e-17</td>\n",
              "      <td>1.245786e-16</td>\n",
              "      <td>1.779694e-17</td>\n",
              "      <td>-9.788316e-17</td>\n",
              "      <td>-1.423755e-16</td>\n",
              "      <td>8.898469e-18</td>\n",
              "      <td>-5.339081e-17</td>\n",
              "      <td>-8.361001e-14</td>\n",
              "      <td>3.025479e-16</td>\n",
              "      <td>8.898469e-17</td>\n",
              "      <td>-5.695020e-16</td>\n",
              "      <td>5.634314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.000313e+00</td>\n",
              "      <td>1.000313e+00</td>\n",
              "      <td>1.000313e+00</td>\n",
              "      <td>1.000313e+00</td>\n",
              "      <td>1.000313e+00</td>\n",
              "      <td>1.000313e+00</td>\n",
              "      <td>1.000313e+00</td>\n",
              "      <td>1.000313e+00</td>\n",
              "      <td>1.000313e+00</td>\n",
              "      <td>1.000313e+00</td>\n",
              "      <td>1.000313e+00</td>\n",
              "      <td>0.806630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-2.136087e+00</td>\n",
              "      <td>-2.280762e+00</td>\n",
              "      <td>-1.391823e+00</td>\n",
              "      <td>-1.169149e+00</td>\n",
              "      <td>-1.604575e+00</td>\n",
              "      <td>-1.422837e+00</td>\n",
              "      <td>-1.262581e+00</td>\n",
              "      <td>-3.546932e+00</td>\n",
              "      <td>-3.709380e+00</td>\n",
              "      <td>-1.937318e+00</td>\n",
              "      <td>-1.899221e+00</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-7.006076e-01</td>\n",
              "      <td>-7.718255e-01</td>\n",
              "      <td>-9.286769e-01</td>\n",
              "      <td>-4.525789e-01</td>\n",
              "      <td>-3.721412e-01</td>\n",
              "      <td>-8.478700e-01</td>\n",
              "      <td>-7.596977e-01</td>\n",
              "      <td>-6.111392e-01</td>\n",
              "      <td>-6.587444e-01</td>\n",
              "      <td>-6.392206e-01</td>\n",
              "      <td>-8.653154e-01</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-2.412541e-01</td>\n",
              "      <td>-4.530063e-02</td>\n",
              "      <td>-5.384522e-02</td>\n",
              "      <td>-2.376079e-01</td>\n",
              "      <td>-1.809014e-01</td>\n",
              "      <td>-1.770746e-01</td>\n",
              "      <td>-2.568145e-01</td>\n",
              "      <td>-6.216372e-04</td>\n",
              "      <td>-9.673073e-03</td>\n",
              "      <td>-2.261897e-01</td>\n",
              "      <td>-2.073755e-01</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.051954e-01</td>\n",
              "      <td>6.253377e-01</td>\n",
              "      <td>7.695258e-01</td>\n",
              "      <td>4.902022e-02</td>\n",
              "      <td>5.283609e-02</td>\n",
              "      <td>4.937208e-01</td>\n",
              "      <td>4.975104e-01</td>\n",
              "      <td>5.780428e-01</td>\n",
              "      <td>5.744912e-01</td>\n",
              "      <td>4.228588e-01</td>\n",
              "      <td>6.385471e-01</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.352281e+00</td>\n",
              "      <td>5.878672e+00</td>\n",
              "      <td>3.754246e+00</td>\n",
              "      <td>9.292776e+00</td>\n",
              "      <td>1.112349e+01</td>\n",
              "      <td>5.380944e+00</td>\n",
              "      <td>3.734821e+00</td>\n",
              "      <td>3.683719e+00</td>\n",
              "      <td>4.533826e+00</td>\n",
              "      <td>7.916418e+00</td>\n",
              "      <td>4.210221e+00</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c49825be-9c4c-4552-a2fd-8b3069842588')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c49825be-9c4c-4552-a2fd-8b3069842588 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c49825be-9c4c-4552-a2fd-8b3069842588');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3966dd96-6e2d-41bb-9763-566648eee2a7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3966dd96-6e2d-41bb-9763-566648eee2a7')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3966dd96-6e2d-41bb-9763-566648eee2a7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"fixed acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 564.4874246034898,\n        \"min\": -2.136087445266697,\n        \"max\": 1597.0,\n        \"samples\": [\n          3.5593875304200383e-17,\n          -0.2412540902819263,\n          1597.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volatile acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 564.4071388908162,\n        \"min\": -2.280761862318004,\n        \"max\": 1597.0,\n        \"samples\": [\n          1.2457856356470134e-16,\n          -0.04530063492271736,\n          1597.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"citric acid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 564.467839279158,\n        \"min\": -1.3918231145643594,\n        \"max\": 1597.0,\n        \"samples\": [\n          1.7796937652100192e-17,\n          -0.053845216225708835,\n          1597.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"residual sugar\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 564.206284465515,\n        \"min\": -1.169149096576392,\n        \"max\": 1597.0,\n        \"samples\": [\n          -9.788315708655106e-17,\n          -0.23760785373784976,\n          1597.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chlorides\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 564.1330532927608,\n        \"min\": -1.604575343589407,\n        \"max\": 1597.0,\n        \"samples\": [\n          -1.4237550121680153e-16,\n          -0.18090141690320155,\n          1597.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"free sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 564.4049762591422,\n        \"min\": -1.422837465630677,\n        \"max\": 1597.0,\n        \"samples\": [\n          8.898468826050096e-18,\n          -0.17707461880381783,\n          1597.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 564.4776363155547,\n        \"min\": -1.2625809077340227,\n        \"max\": 1597.0,\n        \"samples\": [\n          -5.339081295630058e-17,\n          -0.2568144633145958,\n          1597.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"density\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 564.5725382111434,\n        \"min\": -3.546932076507084,\n        \"max\": 1597.0,\n        \"samples\": [\n          -8.36100130895667e-14,\n          -0.0006216371754582388,\n          1597.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 564.541855342906,\n        \"min\": -3.7093798235152384,\n        \"max\": 1597.0,\n        \"samples\": [\n          3.0254794008570326e-16,\n          -0.009673073487641357,\n          1597.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sulphates\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 564.3024459294808,\n        \"min\": -1.9373175009058403,\n        \"max\": 1597.0,\n        \"samples\": [\n          8.898468826050096e-17,\n          -0.22618971565834944,\n          1597.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 564.4822739927429,\n        \"min\": -1.8992208906540098,\n        \"max\": 1597.0,\n        \"samples\": [\n          -5.695020048672061e-16,\n          -0.2073755344278481,\n          1597.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quality\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 562.8894146584076,\n        \"min\": 0.8066296314181137,\n        \"max\": 1597.0,\n        \"samples\": [\n          1597.0,\n          5.634314339386349,\n          6.0\n        ],\n        \"num_unique_values\": 7,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the dataset to see the effects of Normalization\n",
        "\n",
        "fig,ax = plt.subplots(1,1,figsize=(16,9))\n",
        "\n",
        "ax = sns.boxplot(dataset)\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=45)\n",
        "\n",
        "ax.set_title('Dataset boxplot after visualization')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 670
        },
        "id": "boqSkqImSsU6",
        "outputId": "e99e3d68-d291-47d8-eb01-cc78aa344ca6"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-10-2030461e8b01>:6: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x900 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "counts = dataset['quality'].value_counts()\n",
        "\n",
        "title = 'Wines ordered according to the quality score'\n",
        "\n",
        "fig,ax = plt.subplots(1,1,figsize=(16,9))\n",
        "\n",
        "ax.bar(list(counts.keys()), counts)\n",
        "\n",
        "ax.set_title(title)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "3dXJay6gU-R8",
        "outputId": "42f142fa-f43e-44fc-92ed-ea08b725a12c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x900 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABRUAAAL8CAYAAAB+lFK3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABdQklEQVR4nO3deZhWdeH//9ewDSAyieuAKLjghoorigjua2qlaGm5S2ZqVKai+TErlXLJj/nLpcwNU5Fy7WMqCaZpKol9XD+QirEpriwCw3Z+f3jN/WWcQTkIDOXjcV1zXc37vO/7fp975kA+uc85VUVRFAEAAAAAWEItmnsBAAAAAMC/F1ERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAPqNu3bqlW7duzb2MfwtVVVXZbbfdmnsZpRx77LGpqqrK+PHjm3spK7WmjoMbb7wxVVVVufHGG5tlTSs7v1uNjRo1KlVVVfnRj37UYHy33XZLVVVV8ywKAGiSqAjA58rf//73VFVVpXfv3k1uv+2221JVVZWqqqq8/vrrjbbPnj07bdu2Tfv27VNXV7e8lwssBysqbv/oRz9KVVVVRo0atdxf6/NocQESAFgxWjX3AgBgRdpmm22y2mqr5e9//3umT5+ejh07Ntj+5z//OVVVVSmKIo888khOOOGEBtv/+te/pq6uLnvvvXeqq6srjwEa+/KXv5yddtoptbW1zb0U/s3dfPPNmTVrVnMvAwBYhE8qAvC50qJFi+y2225ZsGBBHn300UbbH3nkkey2225ZffXV88gjjzS5PUn23HPPytiGG26YDTfccPktGv5N1dTUZNNNN01NTU1zL4V/c+utt1423XTT5l4GALAIURGAz536IPjxaDh+/Pi8/vrr2XPPPdO/f/+MHDmy0WObioqfdi25kSNHZrfddsuqq66ajh075sADD8zLL7/c5NpmzZqViy++OL169coqq6ySDh06ZOedd85tt93WaG5RFLnpppvSp0+frLnmmmnbtm26du2afffdN3fccccSvx/Tpk3L4MGDs8kmm6Rt27ZZbbXVsu+++2bEiBGN5i56uuHTTz+dAw88MJ06dWpwXbi5c+fmJz/5STbccMNUV1ene/fu+eEPf/iJp4vPnz8/v/rVr7LTTjulY8eOad++fbbZZptcddVVWbhwYYO548ePT1VVVY499tiMHTs2RxxxRNZaa620aNGiwWmmDz74YA444ICsscYaqa6uzoYbbpgf/OAH+eCDD5pcw4gRI7LrrrtmlVVWSadOnfKlL30pr7zyyhK/j/X+/ve/5zvf+U623nrrdOrUKW3bts3GG2+c73//+3n//fcX+7g77rgje+65Z+Ux3bp1y9e+9rWMHj16qefW1dVlyJAh2XLLLdO+fft07Ngxu+66a4YNG9boOZfkfS2KIldddVW22GKLtG3bNl26dMmpp56aadOmNblPi7umYv0x8+GHH+YHP/hB1ltvvVRXV2ejjTbKz372sxRF0ei5iqLIf//3f2fzzTdv9NpLel3T+vUkyaOPPlq51EFTp9AOGzYs/fr1S01NTdq1a5ctt9wyF1988RJf9qBbt2654IILkiS77757g9dqyrXXXpstt9wybdu2zdprr52BAwcu9n2dOHFiTj311GywwQaprq7O6quvnoMPPjjPPPPMEq2t3if9PJt6Tz/pdO5Ff38WNXbs2Jx99tnZfvvts+aaa6a6ujrrr79+Bg4cmIkTJy7xWj9+TcVjjz02u+++e5LkggsuaPD+jho1Ktdee22qqqoqP4OPe/PNN9O6detsueWWS/T69957b/bcc8/U1tamuro6nTt3Tv/+/fOrX/2q0dz33nsv5557bnr27Jn27dunpqYmW2+9dc4+++x8+OGHDeaOGzcuRx99dLp06ZI2bdqkc+fOOfroozNu3LhGz7vo+/+73/0uvXv3TocOHRr8nMr8HQIAn5XTnwH43Nljjz2SND5tuf77PfbYIzU1NfnDH/6Ql156KZtvvnmSZPr06Rk9enRWW221bLvttkv0Wvfff3/uueee7L///jn55JPz0ksv5X/+53/yzDPP5KWXXsoaa6xRmfvBBx9kjz32yJgxY7Ltttvm+OOPz8KFC/Pggw/myCOPzIsvvpif/vSnlfnnnntuLr744nTv3j2HH354ampqMmXKlDzzzDO58847c8QRR3zq+j744IPssssueemll7LDDjtk0KBBeeeddzJs2LDss88+ufrqq/PNb36z0eOefPLJXHzxxenbt2+OP/74vPPOO2nTpk2Kosjhhx+ee+65JxtuuGFOPfXUzJ07N7/97W/z/PPPN7mGefPm5aCDDsqDDz6YTTbZJEceeWTatm2bkSNH5rTTTstTTz2VW265pdHjXn311fTu3Ts9evTIUUcdldmzZ1dOZ7/gggvyox/9KJ06dcoXv/jFrLXWWvnf//3fXHrppfmf//mfPPnkkw1OfR8+fHiOOOKItGnTJkcccURqa2vz+OOPZ+edd85WW231qe/jon7961/nrrvuSv/+/bPXXntl4cKF+fvf/57LL788DzzwQJ566qmsuuqqlflFUeS4447LTTfdlDXWWCNf+cpXsuaaa2bixIkZOXJkNtlkk2y//fal586dOzf77rtvHn300Wy66ab59re/nVmzZlX29bnnnstFF11U6n0dNGhQrrzyytTW1mbgwIFp3bp17rnnnjz11FOZO3du2rRps8Tv07x587Lvvvtm8uTJ2X///dOqVavcfffdOfvsszNnzpycf/75DeZ/+9vfztVXX53OnTtn4MCBadOmTe699948/fTTmTdvXlq3bv2pr9mrV6+cf/75ueCCC7L++us3CGCLXmPxnHPOycUXX5w11lgjRx55ZDp06JAHHngg55xzTh588ME89NBDn7qvgwYNyt13351HH300xxxzzCdGzzPPPDMPPvhgDjrooOyzzz4ZOXJkfv3rX+ef//xno3/8ePbZZ7PPPvvkvffey7777puvfOUreeedd3L33Xenb9++ueuuu3LAAQd86ntRv8Zl9fNcnD/84Q+55pprsvvuu6dPnz5p06ZNXnzxxfzmN7/Jfffdl9GjR6dLly6ln/dLX/pSkuSmm25K//79G/z8unXrlu233z5nnnlmrr/++vzwhz9My5YtGzz+t7/9bebPn9/kn28fd9111+Wb3/xm1llnnRx00EFZY401MnXq1Pzv//5vbrjhhpxyyimVua+//np23333vPHGG9luu+3yrW99KwsXLszYsWPzi1/8IieffHJWWWWVJMkzzzyTvfbaKzNmzMjBBx+czTffPK+88kqGDh2ae+65JyNGjMgOO+zQaD2XXXZZHn744Rx00EHZfffdK/G57N8hAPCZFQDwOVRbW1tUVVUVU6dOrYwdeeSRRYcOHYp58+YVL7zwQpGk+OUvf1nZfu+99xZJii9/+csNnmv99dcv1l9//QZjN9xwQ5GkaNmyZTFixIgG284+++wiSfGzn/2swfgxxxzT5Pjs2bOLfffdt6iqqirGjBlTGe/UqVPRpUuX4sMPP2y0f2+//fYSvQ8DBw4skhQDBw4sFi5cWBkfO3Zs0bFjx6JNmzbF66+/XhkfOXJkkaRIUlxzzTWNnu/WW28tkhQ77bRTMXv27Mr4u+++W2ywwQZFkqJ///4NHnP++ecXSYpTTz21mD9/fmV8/vz5xfHHH18kKe6+++7K+Ouvv15Zw+DBgxut4ZFHHimSFDvvvHPx/vvvN9hW/3MZNGhQZWzGjBlFp06dilatWhXPPPNMg/mDBg2qvNai78MnGT9+fIP9qPeb3/ymSFIMGTKkwfi1115bJCl22GGH4oMPPmiwbf78+cXkyZOXau5FF11UJCn233//Yt68eZXxt956q1h//fWLJMVf//rXyvinva9//etfiyTFhhtuWLz77ruV8dmzZxc77bRTkWSxx8ENN9zQYLz+9ffff/9i1qxZDdZWU1NT1NTUFHPnzq2M/+UvfymSFD169GjwM62rqyt23XXXJl/7kzT1e1jviSeeKJIUXbt2LaZMmVIZnzdvXvHFL36xSFJceOGFS/Q69b/bI0eObHJ7/THftWvX4o033mjwWvX79dRTTzUY33DDDYvq6upi1KhRDZ5r0qRJRefOnYt11lmnmDNnzqeubWl+np+0P/W/P8ccc0yD8YkTJza5ngcffLBo0aJFcfLJJzcYr/8z5vzzz28w3r9//+Lj/+myuLn1vv3tbxdJivvuu6/B+MKFC4vu3bsX7du3b3QcNWXbbbct2rRpU7z11luNtn38z9qdd965SFJcdNFFTc6t/3Nx4cKFxaabblokKYYOHdpg3u23314kKTbZZJNiwYIFlfH69799+/bFs88+2+j5y/4dAgCflagIwOfS17/+9SJJcccdd1TGamtri/3337/y/VprrdUgINYHpquuuqrBc31SVDzqqKMavfZrr71WJCkOPfTQytg777xTtGzZsth+++2bXO9zzz1XJCl+8IMfVMY6depUdOvWbYkCQlPq6uqK9u3bFx06dGgQFer98Ic/LJIUF1xwQWWs/j/ie/Xq1eRz7rXXXkWS4pFHHmm0rf49WTTmLFiwoOjUqVOxzjrrNAhf9d5///2iqqqqGDBgQGWsPl6svfbaTe77l770pSJJ8cILLzS5xl69ehVrrrlm5fuhQ4cWSYqjjz660dwPPvigqKmpKRUVF2fhwoVFx44di913373BeM+ePYskTUaCjyszd6ONNiqqqqqKl19+udG2+sB53HHHVcY+7X098cQTiyTFb3/720bb6n8vykbFcePGNXquo48+ukhSPP/885WxE044oUhS3HTTTY3mP/7448s0Ktbv57XXXtto2//93/8VLVq0KLp3775Er7OkUfHXv/51o22//e1vG/3Dxt13310kKc4444wmn++KK64okhR//OMfP3VtS/PzXJqo+Em23HLLRu/lsoyK9f849MUvfrHB+J/+9KdGv/+fZNttty3at29fvPfee584b/To0ZU/HxeNgU2p/73deeedm9zet2/fIknx6KOPVsbq3/9F/1Gk3tL8HQIAn5XTnwH4XNpjjz0ydOjQPPLIIzn88MPz8ssvZ8qUKfnud79bmbPbbrvl4YcfzsKFC9OiRYsmr6f4aepPRV1U165dk6TB9fWeeeaZLFiwoMlruyUfnSqapMG1GI866qj88pe/zOabb57DDz88/fv3z84777zEN8X4v//7v8yaNSu77LJLOnXq1Gj7HnvskZ/+9KcZM2ZMo2077rhjk8/57LPPpkWLFunbt2+jbYuenlhv7Nixee+997Lxxhsv9rS8du3aNXkNyq233rpyB+5FPfnkk2ndunXuvPPO3HnnnY22z507N2+//XbefffdrL766nn22WeTJP379280t6amJr169Wrypj6LM2/evFx77bW5/fbb89JLL2XatGkNrgs5adKkyv/+8MMP88ILL2TttdfONtts84nPW2bujBkz8s9//jNdunRp8uYW9ZcAaOpnu7j39ZPep759+zY6vfTT1NTUZKONNmo03tTxUb/Opn6vdtppp7Rqtez+L239fta/R4vq0aNH1l133bz++uuZNm3aMrsBzZL+OfHkk08mSd54440m/5yovw7fyy+//KmnQC/rn+fiFEWRW2+9NTfeeGP+8Y9/5P3338+CBQsq25fFKdaLs8UWW6Rfv3554IEHMmHChMp7et111yVJTj755CV6nqOOOirf//73s/nmm+erX/1q+vfvn1122SVrrrlmg3l/+9vfkiT77rtvWrT45EvXf9LvWf34448/njFjxqRfv34NtjX15+/S/B0CAJ+VqAjA51J9GKy/juKi11Ost9tuu2XYsGEZM2ZM1ltvvTz//POLjTSL84UvfKHRWH0AWfQ/rN99990kH/2H4SfdbGHmzJmV//2LX/wiG2ywQW644YYMGTIkQ4YMSatWrXLAAQfksssuazLYLKr+Oly1tbVNbq8fb+rGJuuss85in7NTp05NXt+uqcfU7/e4ceMWe0OFpOF+f9oa3n333cyfP/8Tn6/+OVdfffXK+7D22ms3OW9xr7M4RxxxRO66665ssMEGOeSQQ7LOOutUIt0VV1zR4EYf9e/tklxTrszc5fWzTZp+n1q1atXg+qBLoqljo/65kobHxye9dsuWLbP66quXeu1PsiTv3b/+9a988MEHyywqlv1zoqlYvqimjpePW9Y/z8X53ve+lyuuuCK1tbXZd99906VLl7Rr1y7JRzfOeeONN5bJ6yzOKaeckr/85S/5zW9+kwsuuCBvvvlm7r333vTq1Wux/zjycd/73veyxhpr5Fe/+lWuvPLKXHHFFamqqkr//v1zySWXVKJwcx6jS/N3CAB8VqIiAJ9L6623XjbccMP885//zIQJE/LII4/kC1/4QoNPgNXfWfSRRx7J+uuvn6IoSn1KsYz6OPHd7343l19++RI9pmXLlhk0aFAGDRqUqVOn5vHHH8/tt9+eO++8My+++GJefPHFJj9x9vHXfPPNN5vcPmXKlAbzFrW4O9jW1NTkvffea/LGGU29Tv1zf/nLX84f/vCHxa61KZ+0hoULF+a9995bouepX8Nbb73V5PbFvT9NGT16dO66667stddeeeCBBxp8gm7hwoX5+c9/3mB+fUxa9NOLi1Nm7vL62SYfvU8bbLBBg23z58/PO++8k3XXXfdT17Y06m8U09RrL1iwIO++++5S3eyjKYu+dxtuuGGj7Z/03i1v9a95zz335OCDD14mz1Xm51n/6bv58+c3er6m4tfUqVNz5ZVXpmfPnnniiSca3KAoyQq5I/FXvvKVrL322rn++uvzX//1X6Vu0LKoo48+OkcffXQ++OCDPPHEE7nrrrvy29/+Nvvuu29eeeWVrLnmms16jC7N3yEA8Fl98ufyAeA/WH0gHDFiREaNGpX+/fs3OGVt0003zTrrrJNHHnlkqU59LmPHHXdMixYt8thjjy3V49daa6185StfybBhw7LHHnvk1VdfzQsvvPCJj9lkk03Svn37/OMf/2gyCIwcOTJJlvhO1/VzFy5cmMcff7zRtlGjRjUa23TTTfOFL3whf/vb3yqn531WO+20U95///28+OKLSzS/fv+aOsV52rRpee6555b4tf/5z38mSQ4++OBGp+Q+/fTTmT17doOxVVZZJT179sxbb73V5KnISzt31VVXzYYbbphJkyZVTold1NL+bJOm36fHH3+8wSfqlrX62N/U79Xf/va3JiPXJ2nRosVi11v/Wk39vv7zn//MxIkT071798V+0nJR9acQL6v3ZqeddkqSpf5zYlFL8/NcbbXVkiQTJkxotG306NGNxl577bUsXLgw++yzT6OgOHHixLz22mtLtfZ6S/L+tm7dOieeeGImTZqU++67L7/5zW/SoUOHHHXUUUv1ml/4whdywAEH5Ne//nWOPfbYvPfee/nLX/6S5P/9fB588MEGlzxoyif9niXlj9HP+ncIACwNURGAz636U51/8Ytf5P333698MnFRu+++ex577LE89NBDSZZfVFxrrbVy1FFHZfTo0fnJT37S5H8kv/rqq3n99deTJHV1dfnrX//aaM68efMqn9Br3779J75mmzZtctRRR2XGjBk577zzGr3WlVdemdatW+cb3/jGEu/HcccdlyQ599xzM2fOnMr4e++91+Q1E1u1apXTTjstU6ZMyemnn94ouiUffWLnpZdeWuI11F8X86STTsrkyZMbbf/www8r1z5LkkMOOSSrrbZafve73zUKIz/60Y8qpykuiW7duiVpHAqmTp2ab3/7200+5vTTT0+SfPOb32z0WgsXLqx8Yqns3OOPPz5FUeQHP/hBg9+nd955Jz/5yU8qc5bUsccemyS58MILG3wKdM6cORk8ePASP8/SOProoyuvveh+z507N+ecc07p51t99dWbDGPJ/3tPfvrTn+btt9+ujC9YsCBnnHFGFi5cmBNOOGGJXydJ/vWvf5VeY1MOOeSQbLjhhvn//r//L//zP//T5Jwnn3wys2bN+tTnWpqfZ/3pwjfccEODkDthwoT8+Mc/bjS//nj4eKScOXNmTjrppNIx+OOW9P0dOHBgWrZsmVNPPTWvv/56jjzyyEaR85OMHDkyRVE0Gp86dWqS//dn7XbbbZc+ffrkueeey89+9rNG8999993Kn4u77LJLNtlkkzz++OMZPnx4g3nDhw/PY489lh49ejR5HdGmlP07BACWBac/A/C5tccee6SqqirPP/985fuP23333XPbbbfl9ddfzyabbLLMTrFsylVXXZVx48blv/7rv3LLLbekb9++WXvttTN58uS8/PLLeeaZZ3Lbbbele/fumT17dvr27ZuNNtoo2223XdZff/3MmTMnDz/8cF5++eUcfPDB2WyzzT71NYcMGZLHHnssV111VZ555pnsvvvueeeddzJs2LDMmDEjV111Vbp3777E+/C1r30td9xxR+6999707NkzhxxySObNm5fhw4dnhx12yKuvvtroMeedd17+8Y9/5Jprrsl9992XPfbYI126dMnUqVMzbty4/PWvf82FF16YzTfffInWsOeee2bIkCEZPHhwNt544xxwwAHp3r17Zs6cmTfeeCOPPvpo+vbtmz/96U9Jkg4dOuS6667LEUcckV133TVHHHFEamtr8/jjj+eFF15Iv379Kp9E+jQ77LBDdtlll/zhD39Inz590rdv37z11lt54IEHsskmm6Rz586NHnPiiSfmscceyy233JKNN944hxxySNZcc81Mnjw5jzzySI4//vjKjRfKzD3jjDPywAMP5J577snWW2+dAw44ILNmzcqdd96ZqVOn5swzz1ziYJF8FEFOO+20/PKXv0zPnj1z2GGHpXXr1rnnnnuy2mqrLfbacMtC//79M3DgwFx33XXZYostcuihh6Z169a57777UlNTk86dO3/qjTEWteeee+b222/PQQcdlG233TatW7dOv3790q9fv/Tp0ydnnnlmfv7zn1f2c5VVVskDDzyQF154IX379s0PfvCDJXqd3XffPS1atMjgwYPzwgsvVD7p98Mf/nCp3ofWrVvnD3/4Q/bdd98ceOCB6dOnT3r16pX27dtnwoQJeeaZZ/Laa69lypQpn/qPCkvz8+zdu3fleNhxxx2zxx575K233sp9992Xfffdt1GoXWeddfLVr341t99+e3r16pV99tkn06ZNy8MPP5y2bdumV69epT4J/HH1fybffvvtad26ddZff/1UVVXlG9/4RtZff/3KvPXWWy8HHnhg7r333iQpferzl7/85XTo0CE77bRTunXrlqIo8thjj+WZZ57Jdtttl7322qsyd+jQodltt91yzjnn5Pe//3122223FEWRcePG5aGHHsorr7ySbt26paqqKjfddFP23nvvHHHEETnkkEOy6aab5v/+7/9y9913Z9VVV83NN99c6ve6zN8hALBMNOetpwGguW211VZFkmKNNdYoFi5c2Gj7uHHjiiRFkuKUU05p8jnWX3/9Yv31128wdsMNNxRJihtuuKHJxyQp+vfv32i8rq6u+OUvf1nsvPPORceOHYs2bdoUXbt2LfbYY4/iF7/4RfHOO+8URVEUc+fOLX72s58V++23X9G1a9eiurq6WGONNYrevXsXV199dVFXV7fE78H7779fnHnmmcVGG21UtGnTpqipqSn22muv4sEHH2w0d+TIkUWS4vzzz1/s89XV1RUXXHBB0b1796JNmzbF+uuvX5xzzjnFnDlzFrvfCxcuLG6++eZijz32KFZbbbWidevWRefOnYtddtmluPDCC4t//etflbmvv/56kaQ45phjPnG/HnvssWLAgAFFbW1t0bp162KNNdYott566+K73/1u8cwzzzSa/9BDDxW77LJL0a5du+ILX/hCcfDBBxcvv/xyccwxxxRJitdff/0TX6/eu+++W3zrW98q1l9//aK6urrYYIMNisGDBxcffvhhk78r9YYOHVr069ev6NixY1FdXV1069atOPLII4u///3vSz139uzZxYUXXlhsscUWRdu2bYsOHToUu+yyS/G73/2u0XMuyfu6cOHC4pe//GWx6aabFm3atClqa2uLU045pfjggw9KHQef9D6cf/75RZJi5MiRDcYXLFhQXH755cUmm2zS6LU7dOhQbL311otd98e99dZbxde+9rVirbXWKlq0aNHk7/Rtt91W7LLLLkWHDh2K6urqYvPNNy9++tOfFrNnz17i1ymKorjllluKrbfeumjbtm3lz5J6n/S79UnH2ltvvVWcddZZxRZbbFG0a9euWGWVVYqNNtqoOPTQQ4tbbrmlmDdv3hKtrezPsyg++vPixBNPLNZcc82iTZs2xRZbbFFce+21i/39+fDDD4tzzjmn2HDDDYvq6upi3XXXLU455ZTinXfeKfr37198/D9HFrffTc0tiqJ4+umniz322KPo2LFjUVVV1eTvTlEUxd13310kKbbffvslem8WdfXVVxdf+tKXiu7duxft2rUrVltttaJXr17Fz372s2L69OmN5r/zzjvFmWeeWfTo0aOorq4uampqiq233ro455xzig8//LDB3FdeeaX4+te/XqyzzjpFq1atinXWWac46qijildeeaXR8y7u2FjUkv4dAgDLQlVRNPFZfgAA+Dcwbty49OjRI1/96ldXyI0/Pi/qT10eP358s65jWfnRj36UCy64IL/5zW+W+PR1AOCTuaYiAAArvTfffLPRzS9mzZqVQYMGJfnoFFVoyowZM3LNNdekU6dO+drXvtbcywGA/xiuqQgAwErviiuuyG233ZbddtsttbW1efPNN/PnP/85EydOzP77758BAwY09xJZyfzxj3/Ms88+m/vuuy9vvfVWLr300k+91iQAsORERQAAVnp77713/vGPf+Shhx7Ke++9l1atWqVHjx45/fTTM2jQoFRVVTX3ElnJ3Hnnnbnpppuy9tprZ/DgwZU7wwMAy4ZrKgIAAAAApbimIgAAAABQiqgIAAAAAJTyH3NNxYULF2by5MlZddVVXVMHAAAAAEoqiiIzZsxI586d06LFJ38W8T8mKk6ePDldu3Zt7mUAAAAAwL+1CRMmZN111/3EOf8xUXHVVVdN8tFOd+zYsZlXAwAAAAD/XqZPn56uXbtWOtsn+Y+JivWnPHfs2FFUBAAAAICltCSXFnSjFgAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAopXRUPPbYY1NVVbXYr0mTJlXmPvHEE+nbt2/at2+fddZZJ6effnpmzpzZ6Dnr6upy1llnpXPnzmnXrl169+6dhx9++LPtGQAAAACwXLQq+4BvfvOb2WuvvRqMFUWRk08+Od26dUuXLl2SJM8991z23HPPbLbZZrn88sszceLEXHrppRk3blweeOCBBo8/9thjM3z48AwaNCgbb7xxbrzxxhxwwAEZOXJk+vbt+xl2DwAAAABY1kpHxZ133jk777xzg7HHH388s2bNylFHHVUZO+ecc7Laaqtl1KhR6dixY5KkW7duOemkk/LQQw9ln332SZI8/fTTuf3223PJJZfkjDPOSJIcffTR6dmzZ84888w88cQTS71zAAAAAMCyt0yuqfi73/0uVVVVOfLII5Mk06dPz8MPP5yvf/3rlaCYfBQLO3TokGHDhlXGhg8fnpYtW2bgwIGVsbZt2+aEE07Ik08+mQkTJiyLJQIAAAAAy8hnjorz5s3LsGHD0qdPn3Tr1i1J8vzzz2f+/PnZfvvtG8xt06ZNevXqlTFjxlTGxowZkx49ejSIj0my4447JvnoNGoAAAAAYOXxmaPigw8+mHfffbfBqc9TpkxJktTW1jaaX1tbm8mTJzeYu7h5SRrMXVRdXV2mT5/e4AsAAAAAWP4+c1T83e9+l9atW+fwww+vjM2ePTtJUl1d3Wh+27ZtK9vr5y5u3qLP9XEXX3xxampqKl9du3b9TPsBAAAAACyZzxQVZ86cmXvuuSf77rtvVl999cp4u3btknz0acKPmzNnTmV7/dzFzVv0uT5u8ODBmTZtWuXLtRcBAAAAYMUofffnRd19992N7vqc/L9Tl+tPg17UlClT0rlz5wZzJ02a1OS8JA3mLqq6urrJTzgCAAAAAMvXZ/qk4q233poOHTrk4IMPbjDes2fPtGrVKqNHj24wPnfu3Dz33HPp1atXZaxXr14ZO3Zso2siPvXUU5XtAAAAAMDKY6mj4ttvv50RI0bky1/+ctq3b99gW01NTfbaa68MHTo0M2bMqIzfcsstmTlzZgYMGFAZO+yww7JgwYJcd911lbG6urrccMMN6d27t2slAgAAAMBKZqlPf77jjjsyf/78Rqc+17vwwgvTp0+f9O/fPwMHDszEiRNz2WWXZZ999sl+++1Xmde7d+8MGDAggwcPztSpU7PRRhvlpptuyvjx43P99dcv7fIAAAAAgOWkqiiKYmkeuPPOO+e1117L5MmT07JlyybnPP744znrrLPy7LPPZtVVV83hhx+eiy++OKuuumqDeXPmzMl5552XoUOH5v33389WW22Vn/zkJ9l3332XeD3Tp09PTU1Npk2blo4dOy7NLgEAAADA51aZvrbUUXFlIyoCAAAAwNIr09c+041aAAAAAIDPH1ERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKKVVcy8AACiv29l/bO4l8DkyfsiBzb0EAABWMj6pCAAAAACUIioCAAAAAKWIigAAAABAKaIiAAAAAFCKqAgAAAAAlCIqAgAAAACliIoAAAAAQCmiIgAAAABQiqgIAAAAAJQiKgIAAAAApYiKAAAAAEApoiIAAAAAUIqoCAAAAACUIioCAAAAAKWIigAAAABAKaIiAAAAAFCKqAgAAAAAlCIqAgAAAACliIoAAAAAQCmiIgAAAABQiqgIAAAAAJQiKgIAAAAApYiKAAAAAEApoiIAAAAAUIqoCAAAAACUIioCAAAAAKWIigAAAABAKaIiAAAAAFCKqAgAAAAAlCIqAgAAAACliIoAAAAAQCmiIgAAAABQiqgIAAAAAJQiKgIAAAAApYiKAAAAAEApoiIAAAAAUIqoCAAAAACUIioCAAAAAKWIigAAAABAKaIiAAAAAFCKqAgAAAAAlNKquRcAAABLq9vZf2zuJfA5MX7Igc29BABYqfikIgAAAABQiqgIAAAAAJQiKgIAAAAApYiKAAAAAEApoiIAAAAAUIqoCAAAAACUIioCAAAAAKWIigAAAABAKaIiAAAAAFCKqAgAAAAAlCIqAgAAAACliIoAAAAAQCmiIgAAAABQiqgIAAAAAJQiKgIAAAAApYiKAAAAAEApoiIAAAAAUIqoCAAAAACUIioCAAAAAKWIigAAAABAKaIiAAAAAFCKqAgAAAAAlCIqAgAAAACliIoAAAAAQCmiIgAAAABQiqgIAAAAAJQiKgIAAAAApYiKAAAAAEApoiIAAAAAUIqoCAAAAACUslRR8dlnn83BBx+cTp06pX379unZs2euvPLKBnOeeOKJ9O3bN+3bt88666yT008/PTNnzmz0XHV1dTnrrLPSuXPntGvXLr17987DDz+8dHsDAAAAACx3rco+4KGHHspBBx2UbbbZJuedd146dOiQV199NRMnTqzMee6557Lnnntms802y+WXX56JEyfm0ksvzbhx4/LAAw80eL5jjz02w4cPz6BBg7LxxhvnxhtvzAEHHJCRI0emb9++n30PAQAAAIBlqlRUnD59eo4++ugceOCBGT58eFq0aPqDjuecc05WW221jBo1Kh07dkySdOvWLSeddFIeeuih7LPPPkmSp59+OrfffnsuueSSnHHGGUmSo48+Oj179syZZ56ZJ5544rPsGwAAAACwHJQ6/fl3v/td3nrrrVx44YVp0aJFPvzwwyxcuLDBnOnTp+fhhx/O17/+9UpQTD6KhR06dMiwYcMqY8OHD0/Lli0zcODAyljbtm1zwgkn5Mknn8yECROWdr8AAAAAgOWkVFQcMWJEOnbsmEmTJmWTTTZJhw4d0rFjx3zrW9/KnDlzkiTPP/985s+fn+23377BY9u0aZNevXplzJgxlbExY8akR48eDeJjkuy4445JPjqNGgAAAABYuZSKiuPGjcv8+fNzyCGHZN99983vf//7HH/88bnmmmty3HHHJUmmTJmSJKmtrW30+Nra2kyePLny/ZQpUxY7L0mDuR9XV1eX6dOnN/gCAAAAAJa/UtdUnDlzZmbNmpWTTz65crfnr3zlK5k7d26uvfba/PjHP87s2bOTJNXV1Y0e37Zt28r2JJk9e/Zi59VvX5yLL744F1xwQZnlAwAAAADLQKlPKrZr1y5J8rWvfa3B+JFHHpkkefLJJytz6urqGj1+zpw5le31z7e4eYu+XlMGDx6cadOmVb5cfxEAAAAAVoxSUbFz585JkrXXXrvB+FprrZUkef/99yunLtefBr2oKVOmVJ4j+eg058XNW/T1mlJdXZ2OHTs2+AIAAAAAlr9SUXG77bZLkkyaNKnBeP21D9dcc8307NkzrVq1yujRoxvMmTt3bp577rn06tWrMtarV6+MHTu20fUQn3rqqcp2AAAAAGDlUioqHn744UmS66+/vsH4b37zm7Rq1Sq77bZbampqstdee2Xo0KGZMWNGZc4tt9ySmTNnZsCAAZWxww47LAsWLMh1111XGaurq8sNN9yQ3r17p2vXrku1UwAAAADA8lPqRi3bbLNNjj/++Pz2t7/N/Pnz079//4waNSp33nlnBg8eXDld+cILL0yfPn3Sv3//DBw4MBMnTsxll12WffbZJ/vtt1/l+Xr37p0BAwZk8ODBmTp1ajbaaKPcdNNNGT9+fKNwCQAAAACsHEpFxSS55pprst566+WGG27IXXfdlfXXXz+/+MUvMmjQoMqcbbfdNiNGjMhZZ52V7373u1l11VVzwgkn5OKLL270fDfffHPOO++83HLLLXn//fez1VZb5f7770+/fv0+044BAAAAAMtHVVEURXMvYlmYPn16ampqMm3aNDdtAeA/Xrez/9jcS+BzZPyQA5t7CYvlWGBFWZmPAwBYVsr0tVLXVAQAAAAAEBUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKCUUlFx1KhRqaqqavLrb3/7W4O5TzzxRPr27Zv27dtnnXXWyemnn56ZM2c2es66urqcddZZ6dy5c9q1a5fevXvn4Ycf/mx7BQAAAAAsN62W5kGnn356dthhhwZjG220UeV/P/fcc9lzzz2z2Wab5fLLL8/EiRNz6aWXZty4cXnggQcaPO7YY4/N8OHDM2jQoGy88ca58cYbc8ABB2TkyJHp27fv0iwPAAAAAFiOlioq7rrrrjnssMMWu/2cc87JaqutllGjRqVjx45Jkm7duuWkk07KQw89lH322SdJ8vTTT+f222/PJZdckjPOOCNJcvTRR6dnz54588wz88QTTyzN8gAAAACA5Wipr6k4Y8aMzJ8/v9H49OnT8/DDD+frX/96JSgmH8XCDh06ZNiwYZWx4cOHp2XLlhk4cGBlrG3btjnhhBPy5JNPZsKECUu7PAAAAABgOVmqqHjcccelY8eOadu2bXbfffeMHj26su3555/P/Pnzs/322zd4TJs2bdKrV6+MGTOmMjZmzJj06NGjQXxMkh133DHJR6dRAwAAAAArl1KnP7dp0yaHHnpoDjjggKyxxhp56aWXcumll2bXXXfNE088kW222SZTpkxJktTW1jZ6fG1tbR577LHK91OmTFnsvCSZPHnyYtdSV1eXurq6yvfTp08vsysAAAAAwFIqFRX79OmTPn36VL4/+OCDc9hhh2WrrbbK4MGD86c//SmzZ89OklRXVzd6fNu2bSvbk2T27NmLnVe/fXEuvvjiXHDBBWWWDwAAAAAsA0t9TcV6G220UQ455JCMHDkyCxYsSLt27ZKkwacI682ZM6eyPUnatWu32Hn12xdn8ODBmTZtWuXL9RcBAAAAYMVYqrs/f1zXrl0zd+7cfPjhh5VTl+tPg17UlClT0rlz58r3tbW1mTRpUpPzkjSY+3HV1dVNfsoRAAAAAFi+PvMnFZPktddeS9u2bdOhQ4f07NkzrVq1anDzliSZO3dunnvuufTq1asy1qtXr4wdO7bR9RCfeuqpynYAAAAAYOVSKiq+/fbbjcb+8Y9/5N57780+++yTFi1apKamJnvttVeGDh2aGTNmVObdcsstmTlzZgYMGFAZO+yww7JgwYJcd911lbG6urrccMMN6d27d7p27bo0+wQAAAAALEelTn8+4ogj0q5du/Tp0ydrrbVWXnrppVx33XVp3759hgwZUpl34YUXpk+fPunfv38GDhyYiRMn5rLLLss+++yT/fbbrzKvd+/eGTBgQAYPHpypU6dmo402yk033ZTx48fn+uuvX3Z7CQAAAAAsM6U+qfilL30p77zzTi6//PKccsopueOOO/KVr3wlo0ePzmabbVaZt+2222bEiBFp165dvvvd7+a6667LCSeckOHDhzd6zptvvjmDBg3KLbfcktNPPz3z5s3L/fffn379+n32vQMAAAAAlrmqoiiK5l7EsjB9+vTU1NRk2rRp6dixY3MvBwCWq25n/7G5l8DnyPghBzb3EhbLscCKsjIfBwCwrJTpa8vkRi0AAAAAwOeHqAgAAAAAlCIqAgAAAACliIoAAAAAQCmiIgAAAABQiqgIAAAAAJQiKgIAAAAApYiKAAAAAEApoiIAAAAAUIqoCAAAAACUIioCAAAAAKWIigAAAABAKaIiAAAAAFCKqAgAAAAAlCIqAgAAAACliIoAAAAAQCmiIgAAAABQiqgIAAAAAJQiKgIAAAAApYiKAAAAAEApoiIAAAAAUIqoCAAAAACUIioCAAAAAKWIigAAAABAKaIiAAAAAFCKqAgAAAAAlCIqAgAAAACliIoAAAAAQCmiIgAAAABQiqgIAAAAAJQiKgIAAAAApYiKAAAAAEApoiIAAAAAUIqoCAAAAACUIioCAAAAAKWIigAAAABAKaIiAAAAAFCKqAgAAAAAlCIqAgAAAACliIoAAAAAQCmiIgAAAABQiqgIAAAAAJQiKgIAAAAApYiKAAAAAEApoiIAAAAAUIqoCAAAAACUIioCAAAAAKWIigAAAABAKaIiAAAAAFCKqAgAAAAAlCIqAgAAAACliIoAAAAAQCmiIgAAAABQiqgIAAAAAJQiKgIAAAAApYiKAAAAAEApoiIAAAAAUIqoCAAAAACUIioCAAAAAKWIigAAAABAKaIiAAAAAFCKqAgAAAAAlCIqAgAAAACliIoAAAAAQCmiIgAAAABQiqgIAAAAAJQiKgIAAAAApYiKAAAAAEApoiIAAAAAUIqoCAAAAACUIioCAAAAAKWIigAAAABAKaIiAAAAAFCKqAgAAAAAlCIqAgAAAACliIoAAAAAQCmiIgAAAABQiqgIAAAAAJQiKgIAAAAApYiKAAAAAEApoiIAAAAAUIqoCAAAAACUIioCAAAAAKV85qh44YUXpqqqKj179my07Yknnkjfvn3Tvn37rLPOOjn99NMzc+bMRvPq6upy1llnpXPnzmnXrl169+6dhx9++LMuDQAAAABYDj5TVJw4cWIuuuiirLLKKo22Pffcc9lzzz0za9asXH755TnxxBNz3XXXZcCAAY3mHnvssbn88stz1FFH5b//+7/TsmXLHHDAAXn88cc/y/IAAAAAgOWg1Wd58BlnnJGddtopCxYsyDvvvNNg2znnnJPVVlsto0aNSseOHZMk3bp1y0knnZSHHnoo++yzT5Lk6aefzu23355LLrkkZ5xxRpLk6KOPTs+ePXPmmWfmiSee+CxLBAAAAACWsaX+pOJf/vKXDB8+PFdccUWjbdOnT8/DDz+cr3/965WgmHwUCzt06JBhw4ZVxoYPH56WLVtm4MCBlbG2bdvmhBNOyJNPPpkJEyYs7RIBAAAAgOVgqaLiggULctppp+XEE0/Mlltu2Wj7888/n/nz52f77bdvMN6mTZv06tUrY8aMqYyNGTMmPXr0aBAfk2THHXdM8tFp1AAAAADAymOpTn++5ppr8sYbb2TEiBFNbp8yZUqSpLa2ttG22traPPbYYw3mLm5ekkyePLnJ16irq0tdXV3l++nTpy/5DgAAAAAAS630JxXffffd/Nd//VfOO++8rLnmmk3OmT17dpKkurq60ba2bdtWttfPXdy8RZ/r4y6++OLU1NRUvrp27Vp2VwAAAACApVA6Kv7whz9Mp06dctpppy12Trt27ZKkwScJ682ZM6eyvX7u4uYt+lwfN3jw4EybNq3y5dqLAAAAALBilDr9edy4cbnuuutyxRVXNDgtec6cOZk3b17Gjx+fjh07Vk5drj8NelFTpkxJ586dK9/X1tZm0qRJTc5L0mDuoqqrq5v8hCMAAAAAsHyV+qTipEmTsnDhwpx++unp3r175eupp57K2LFj07179/z4xz9Oz54906pVq4wePbrB4+fOnZvnnnsuvXr1qoz16tUrY8eObXRNxKeeeqqyHQAAAABYeZSKij179sxdd93V6GuLLbbIeuutl7vuuisnnHBCampqstdee2Xo0KGZMWNG5fG33HJLZs6cmQEDBlTGDjvssCxYsCDXXXddZayuri433HBDevfu7VqJAAAAALCSKXX68xprrJEvfelLjcavuOKKJGmw7cILL0yfPn3Sv3//DBw4MBMnTsxll12WffbZJ/vtt19lXu/evTNgwIAMHjw4U6dOzUYbbZSbbrop48ePz/XXX79UOwUAAAAALD+lb9SypLbddtuMGDEi7dq1y3e/+91cd911OeGEEzJ8+PBGc2+++eYMGjQot9xyS04//fTMmzcv999/f/r167e8lgcAAAAALKWqoiiK5l7EsjB9+vTU1NRk2rRp6dixY3MvBwCWq25n/7G5l8DnyPghBzb3EhbLscCKsjIfBwCwrJTpa8vtk4oAAAAAwH8mUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEopFRVffPHFDBgwIBtssEHat2+fNdZYI/369ct9993XaO7LL7+c/fbbLx06dEinTp3yjW98I2+//XajeQsXLszPf/7zdO/ePW3bts1WW22V2267ben3CAAAAABYrlqVmfzGG29kxowZOeaYY9K5c+fMmjUrv//973PwwQfn2muvzcCBA5MkEydOTL9+/VJTU5OLLrooM2fOzKWXXprnn38+Tz/9dNq0aVN5znPPPTdDhgzJSSedlB122CH33HNPjjzyyFRVVeWrX/3qst1bAAAAAOAzqyqKovgsT7BgwYJst912mTNnTl555ZUkySmnnJIbb7wxr7zyStZbb70kyYgRI7L33ns3iI+TJk1K9+7dM3DgwFx11VVJkqIo0r9//7z++usZP358WrZsuUTrmD59empqajJt2rR07Njxs+wSAKz0up39x+ZeAp8j44cc2NxLWCzHAivKynwcAMCyUqavfeZrKrZs2TJdu3bNBx98UBn7/e9/ny9+8YuVoJgke+21V3r06JFhw4ZVxu65557Mmzcvp5xySmWsqqoq3/rWtzJx4sQ8+eSTn3V5AAAAAMAytlRR8cMPP8w777yTV199Nb/4xS/ywAMPZM8990zy0acPp06dmu23377R43bccceMGTOm8v2YMWOyyiqrZLPNNms0r347AAAAALByKXVNxXrf//73c+211yZJWrRoka985SuV05enTJmSJKmtrW30uNra2rz33nupq6tLdXV1pkyZkrXXXjtVVVWN5iXJ5MmTF7uGurq61NXVVb6fPn360uwKAAAAAFDSUn1ScdCgQXn44Ydz0003Zf/998+CBQsyd+7cJMns2bOTJNXV1Y0e17Zt2wZzZs+evUTzmnLxxRenpqam8tW1a9el2RUAAAAAoKSlioqbbrpp9tprrxx99NG5//77M3PmzBx00EEpiiLt2rVLkgafIqw3Z86cJKnMadeu3RLNa8rgwYMzbdq0yteECROWZlcAAAAAgJI+841akuSwww7LM888k7Fjx1ZOXa4/DXpRU6ZMSadOnSqfTqytrc2bb76Zj9+Auv6xnTt3XuxrVldXp2PHjg2+AAAAAIDlb5lExfrTlKdNm5YuXbpkzTXXzOjRoxvNe/rpp9OrV6/K97169cqsWbPy8ssvN5j31FNPVbYDAAAAACuXUlFx6tSpjcbmzZuXm2++Oe3atcvmm2+eJDn00ENz//33Nzgl+c9//nPGjh2bAQMGVMYOOeSQtG7dOr/61a8qY0VR5JprrkmXLl3Sp0+f0jsEAAAAACxfpe7+/M1vfjPTp09Pv3790qVLl7z55pu59dZb88orr+Syyy5Lhw4dkiTnnHNO7rzzzuy+++75zne+k5kzZ+aSSy7JlltumeOOO67yfOuuu24GDRqUSy65JPPmzcsOO+yQu+++O4899lhuvfXWtGzZctnuLQAAAADwmZWKikcccUSuv/76XH311Xn33Xez6qqrZrvttsvPfvazHHzwwZV5Xbt2zaOPPprvfe97Ofvss9OmTZsceOCBueyyyxrd7XnIkCFZbbXVcu211+bGG2/MxhtvnKFDh+bII49cNnsIAAAAACxTVcXH75Lyb2r69OmpqanJtGnT3LQFgP943c7+Y3Mvgc+R8UMObO4lLJZjgRVlZT4OAGBZKdPXlsmNWgAAAACAzw9REQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAASikVFZ955pmceuqp2WKLLbLKKqtkvfXWy+GHH56xY8c2mvvyyy9nv/32S4cOHdKpU6d84xvfyNtvv91o3sKFC/Pzn/883bt3T9u2bbPVVlvltttuW/o9AgAAAACWq1ZlJv/sZz/LX//61wwYMCBbbbVV3nzzzVx11VXZdttt87e//S09e/ZMkkycODH9+vVLTU1NLrroosycOTOXXnppnn/++Tz99NNp06ZN5TnPPffcDBkyJCeddFJ22GGH3HPPPTnyyCNTVVWVr371q8t2bwEAAACAz6yqKIpiSSc/8cQT2X777RtEwXHjxmXLLbfMYYcdlqFDhyZJTjnllNx444155ZVXst566yVJRowYkb333jvXXnttBg4cmCSZNGlSunfvnoEDB+aqq65KkhRFkf79++f111/P+PHj07JlyyVa2/Tp01NTU5Np06alY8eOS7pLAPBvqdvZf2zuJfA5Mn7Igc29hMVyLLCirMzHAQAsK2X6WqnTn/v06dMgKCbJxhtvnC222CIvv/xyZez3v/99vvjFL1aCYpLstdde6dGjR4YNG1YZu+eeezJv3ryccsoplbGqqqp861vfysSJE/Pkk0+WWR4AAAAAsAJ85hu1FEWRt956K2ussUaSjz59OHXq1Gy//faN5u64444ZM2ZM5fsxY8ZklVVWyWabbdZoXv32xamrq8v06dMbfAEAAAAAy99njoq33nprJk2alCOOOCJJMmXKlCRJbW1to7m1tbV57733UldXV5m79tprp6qqqtG8JJk8efJiX/fiiy9OTU1N5atr166fdVcAAAAAgCXwmaLiK6+8km9/+9vZeeedc8wxxyRJZs+enSSprq5uNL9t27YN5syePXuJ5jVl8ODBmTZtWuVrwoQJn2VXAAAAAIAlVOruz4t68803c+CBB6ampibDhw+v3FClXbt2SVL5NOKi5syZ02BOu3btlmheU6qrq5sMkgAAAADA8rVUUXHatGnZf//988EHH+Sxxx5L586dK9vqT12uPw16UVOmTEmnTp0qMbC2tjYjR45MURQNToGuf+yizwsAAEBj7oLOiuRO6EC90qc/z5kzJwcddFDGjh2b+++/P5tvvnmD7V26dMmaa66Z0aNHN3rs008/nV69elW+79WrV2bNmtXgztFJ8tRTT1W2AwAAAAArl1JRccGCBTniiCPy5JNP5s4778zOO+/c5LxDDz00999/f4PrHP75z3/O2LFjM2DAgMrYIYccktatW+dXv/pVZawoilxzzTXp0qVL+vTpU3Z/AAAAAIDlrNTpz9///vdz77335qCDDsp7772XoUOHNtj+9a9/PUlyzjnn5M4778zuu++e73znO5k5c2YuueSSbLnlljnuuOMq89ddd90MGjQol1xySebNm5cddtghd999dx577LHceuutles0AgAAAAArj1JR8bnnnkuS3Hfffbnvvvsaba+Pil27ds2jjz6a733vezn77LPTpk2bHHjggbnssssa3VxlyJAhWW211XLttdfmxhtvzMYbb5yhQ4fmyCOPXMpdAgAAAACWp1JRcdSoUUs8d4sttsiDDz74qfNatGiRwYMHZ/DgwWWWAgAAAAA0k9I3agEAAAAAPt9ERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKCU0lFx5syZOf/887PffvulU6dOqaqqyo033tjk3Jdffjn77bdfOnTokE6dOuUb3/hG3n777UbzFi5cmJ///Ofp3r172rZtm6222iq33XZb6Z0BAAAAAJa/0lHxnXfeyY9//OO8/PLL2XrrrRc7b+LEienXr1/++c9/5qKLLsoZZ5yRP/7xj9l7770zd+7cBnPPPffcnHXWWdl7773zy1/+Muutt16OPPLI3H777eX3CAAAAABYrlqVfUBtbW2mTJmSddZZJ6NHj84OO+zQ5LyLLrooH374Yf7+979nvfXWS5LsuOOO2XvvvXPjjTdm4MCBSZJJkyblsssuy7e//e1cddVVSZITTzwx/fv3zw9+8IMMGDAgLVu2XNr9AwAAAACWsdKfVKyurs4666zzqfN+//vf54tf/GIlKCbJXnvtlR49emTYsGGVsXvuuSfz5s3LKaecUhmrqqrKt771rUycODFPPvlk2SUCAAAAAMvRcrlRy6RJkzJ16tRsv/32jbbtuOOOGTNmTOX7MWPGZJVVVslmm23WaF79dgAAAABg5VH69OclMWXKlCQfnSr9cbW1tXnvvfdSV1eX6urqTJkyJWuvvXaqqqoazUuSyZMnN/kadXV1qaurq3w/ffr0ZbV8AAAAAOATLJdPKs6ePTvJR6dKf1zbtm0bzJk9e/YSzfu4iy++ODU1NZWvrl27LpO1AwAAAACfbLlExXbt2iVJg08S1pszZ06DOe3atVuieR83ePDgTJs2rfI1YcKEZbJ2AAAAAOCTLZfTn+tPXa4/DXpRU6ZMSadOnSqfTqytrc3IkSNTFEWDU6DrH9u5c+cmX6O6urrJTzgCAAAAAMvXcvmkYpcuXbLmmmtm9OjRjbY9/fTT6dWrV+X7Xr16ZdasWXn55ZcbzHvqqacq2wEAAACAlcdyiYpJcuihh+b+++9vcFryn//854wdOzYDBgyojB1yyCFp3bp1fvWrX1XGiqLINddcky5duqRPnz7La4kAAAAAwFJYqtOfr7rqqnzwwQeVOzPfd999mThxYpLktNNOS01NTc4555zceeed2X333fOd73wnM2fOzCWXXJItt9wyxx13XOW51l133QwaNCiXXHJJ5s2blx122CF33313Hnvssdx6661p2bLlMthNAAAAAGBZWaqoeOmll+aNN96ofP+HP/whf/jDH5IkX//61yt3Y3700Ufzve99L2effXbatGmTAw88MJdddlmjayEOGTIkq622Wq699trceOON2XjjjTN06NAceeSRn2HXAAAAAIDlYami4vjx45do3hZbbJEHH3zwU+e1aNEigwcPzuDBg5dmOQAAAADACrTcrqkIAAAAAPxnEhUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJaNfcCAMrqdvYfm3sJfI6MH3Jgcy8BAABgpeOTigAAAABAKaIiAAAAAFCKqAgAAAAAlCIqAgAAAACliIoAAAAAQCmiIgAAAABQiqgIAAAAAJQiKgIAAAAApYiKAAAAAEApoiIAAAAAUIqoCAAAAACUIioCAAAAAKWIigAAAABAKaIiAAAAAFCKqAgAAAAAlCIqAgAAAACliIoAAAAAQCmiIgAAAABQiqgIAAAAAJQiKgIAAAAApYiKAAAAAEApoiIAAAAAUIqoCAAAAACUIioCAAAAAKWIigAAAABAKaIiAAAAAFCKqAgAAAAAlCIqAgAAAACliIoAAAAAQCmiIgAAAABQiqgIAAAAAJQiKgIAAAAApYiKAAAAAEAprZp7AQAAAACfRbez/9jcS+BzZPyQA5t7CSsFn1QEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACglFbNvQDK6Xb2H5t7CXxOjB9yYHMvAQAAAFhJ+aQiAAAAAFCKqAgAAAAAlLJSRMW6urqcddZZ6dy5c9q1a5fevXvn4Ycfbu5lAQAAAABNWCmi4rHHHpvLL788Rx11VP77v/87LVu2zAEHHJDHH3+8uZcGAAAAAHxMs9+o5emnn87tt9+eSy65JGeccUaS5Oijj07Pnj1z5pln5oknnmjmFQIAAAAAi2r2TyoOHz48LVu2zMCBAytjbdu2zQknnJAnn3wyEyZMaMbVAQAAAAAf1+xRccyYMenRo0c6duzYYHzHHXdMkjz33HPNsCoAAAAAYHGa/fTnKVOmpLa2ttF4/djkyZObfFxdXV3q6uoq30+bNi1JMn369OWwypXHwrpZzb0EPidW5mPJccCKtLIeC44DVqSV9ThIHAusOI4D+MjKeiw4DliRVtbjYFmo37eiKD51brNHxdmzZ6e6urrReNu2bSvbm3LxxRfnggsuaDTetWvXZbtA+JyquaK5VwArB8cCOA4gcRxAPccCfD6OgxkzZqSmpuYT5zR7VGzXrl2DTxzWmzNnTmV7UwYPHpzvfe97le8XLlyY9957L6uvvnqqqqqWz2L5tzN9+vR07do1EyZMaHSKPXyeOBbAcQD1HAvgOIDEcUDTiqLIjBkz0rlz50+d2+xRsba2NpMmTWo0PmXKlCRZ7E5UV1c3+oTjF77whWW+Pv4zdOzY0R+SEMcCJI4DqOdYAMcBJI4DGvu0TyjWa/YbtfTq1Stjx45tdD76U089VdkOAAAAAKw8mj0qHnbYYVmwYEGuu+66ylhdXV1uuOGG9O7d2zUSAQAAAGAl0+ynP/fu3TsDBgzI4MGDM3Xq1Gy00Ua56aabMn78+Fx//fXNvTz+zVVXV+f8889v8mZA8HniWADHAdRzLIDjABLHAZ9dVbEk94hezubMmZPzzjsvQ4cOzfvvv5+tttoqP/nJT7Lvvvs299IAAAAAgI9ZKaIiAAAAAPDvo9mvqQgAAAAA/HsRFQEAAACAUkRFAAAAAKAUUZH/OC+++GIGDBiQDTbYIO3bt88aa6yRfv365b777mvupUGzuvDCC1NVVZWePXs291JghRk1alSqqqqa/Prb3/7W3MuDFerZZ5/NwQcfnE6dOqV9+/bp2bNnrrzyyuZeFqwwxx577GL/TqiqqsqkSZOae4mwQowbNy5f/epXs+6666Z9+/bZdNNN8+Mf/zizZs1q7qXxb6ZVcy8AlrU33ngjM2bMyDHHHJPOnTtn1qxZ+f3vf5+DDz441157bQYOHNjcS4QVbuLEibnooouyyiqrNPdSoFmcfvrp2WGHHRqMbbTRRs20GljxHnrooRx00EHZZpttct5556VDhw559dVXM3HixOZeGqww3/zmN7PXXns1GCuKIieffHK6deuWLl26NNPKYMWZMGFCdtxxx9TU1OTUU09Np06d8uSTT+b888/P3//+99xzzz3NvUT+jbj7M58LCxYsyHbbbZc5c+bklVdeae7lwAr31a9+NW+//XYWLFiQd955Jy+88EJzLwlWiFGjRmX33XfPnXfemcMOO6y5lwPNYvr06enRo0f69OmT4cOHp0ULJytBvccffzy77rprLrzwwpxzzjnNvRxY7i666KKce+65eeGFF7LFFltUxo855pjcfPPNee+997Laaqs14wr5d+L/UfC50LJly3Tt2jUffPBBcy8FVri//OUvGT58eK644ormXgo0qxkzZmT+/PnNvQxY4X73u9/lrbfeyoUXXpgWLVrkww8/zMKFC5t7WbBS+N3vfpeqqqoceeSRzb0UWCGmT5+eJFl77bUbjNfW1qZFixZp06ZNcyyLf1OiIv+xPvzww7zzzjt59dVX84tf/CIPPPBA9txzz+ZeFqxQCxYsyGmnnZYTTzwxW265ZXMvB5rNcccdl44dO6Zt27bZfffdM3r06OZeEqwwI0aMSMeOHTNp0qRssskm6dChQzp27JhvfetbmTNnTnMvD5rNvHnzMmzYsPTp0yfdunVr7uXACrHbbrslSU444YQ899xzmTBhQu64445cffXVOf30010uiVJcU5H/WN///vdz7bXXJklatGiRr3zlK7nqqquaeVWwYl1zzTV54403MmLEiOZeCjSLNm3a5NBDD80BBxyQNdZYIy+99FIuvfTS7LrrrnniiSeyzTbbNPcSYbkbN25c5s+fn0MOOSQnnHBCLr744owaNSq//OUv88EHH+S2225r7iVCs3jwwQfz7rvv5qijjmrupcAKs99+++UnP/lJLrrootx7772V8XPPPTc//elPm3Fl/DtyTUX+Y73yyiuZOHFiJk+enGHDhqVNmza5+uqrG33MG/5Tvfvuu+nRo0fOOeecfP/730/y0b9MuqYin3f//Oc/s9VWW6Vfv37505/+1NzLgeVuww03zGuvvZaTTz45V199dWX85JNPzrXXXpuxY8dm4403bsYVQvM48sgjM3z48EyZMiWrr756cy8HVpihQ4dm6NChOfTQQ7P66qvnj3/8Y2644YZceeWVOfXUU5t7efwbERX53Nhnn33ywQcf5KmnnkpVVVVzLweWu29961sZMWJEXnzxxcq1UURF+MjXvva1/OEPf8isWbPSsmXL5l4OLFc9e/bMiy++mEcffTT9+vWrjP/lL39J//79c9NNN+Xoo49uxhXCijdz5sysvfba2WOPPXLfffc193Jghbn99ttz/PHHZ+zYsVl33XUr48cdd1yGDRuWf/3rXyI7S8w1FfncOOyww/LMM89k7Nixzb0UWO7GjRuX6667LqeffnomT56c8ePHZ/z48ZkzZ07mzZuX8ePH57333mvuZUKz6dq1a+bOnZsPP/ywuZcCy13nzp2TNL4o/1prrZUkef/991f4mqC53X333Zk1a5ZTn/nc+dWvfpVtttmmQVBMkoMPPjizZs3KmDFjmmll/DsSFfncmD17dpJk2rRpzbwSWP4mTZqUhQsX5vTTT0/37t0rX0899VTGjh2b7t2758c//nFzLxOazWuvvZa2bdumQ4cOzb0UWO622267JB/93bCoyZMnJ0nWXHPNFb4maG633nprOnTokIMPPri5lwIr1FtvvZUFCxY0Gp83b16SZP78+St6SfwbExX5jzN16tRGY/PmzcvNN9+cdu3aZfPNN2+GVcGK1bNnz9x1112NvrbYYoust956ueuuu3LCCSc09zJhuXv77bcbjf3jH//Ivffem3322SctWvi/QvznO/zww5Mk119/fYPx3/zmN2nVqlXlTqDwefH2229nxIgR+fKXv5z27ds393JgherRo0fGjBnT6Ay+2267LS1atMhWW23VTCvj35G7P/Mf55vf/GamT5+efv36pUuXLnnzzTdz66235pVXXslll13mUyl8Lqyxxhr50pe+1Gj8iiuuSJImt8F/oiOOOCLt2rVLnz59stZaa+Wll17Kddddl/bt22fIkCHNvTxYIbbZZpscf/zx+e1vf5v58+enf//+GTVqVO68884MHjy4cno0fF7ccccdmT9/vlOf+Vz6wQ9+kAceeCC77rprTj311Ky++uq5//7788ADD+TEE0/0dwKluFEL/3Fuv/32XH/99Xn++efz7rvvZtVVV812222X0047zekNfO65UQufN1deeWVuvfXW/POf/8z06dOz5pprZs8998z555+fjTbaqLmXByvMvHnzctFFF+WGG27I5MmTs/766+fb3/52Bg0a1NxLgxVu5513zmuvvZbJkye7WRefS08//XR+9KMfZcyYMXn33XfTvXv3HHPMMTnzzDPTqpXPnrHkREUAAAAAoBQXEgIAAAAAShEVAQAAAIBSREUAAAAAoBRREQAAAAAoRVQEAAAAAEoRFQEAAACAUkRFAAAAAKAUUREAAAAAKEVUBAAAAABKERUBAAAAgFJERQAAAACgFFERAAAAAChFVAQAAAAASvn/AWle3CqtDj3dAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Add a new column distinguishing good wines\n",
        "\n",
        "dataset[['boolQuality']] = 0\n",
        "\n",
        "dataset['boolQuality'][dataset['quality'] > 5] = 1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p_kCEwNYV3A1",
        "outputId": "68cab8b2-150a-4317-dc7e-adb8de48a176"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-12-670bb5471d09>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  dataset['boolQuality'][dataset['quality'] > 5] = 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "counts = dataset['boolQuality'].value_counts()\n",
        "\n",
        "fig,ax = plt.subplots(1,1,figsize=(16,9))\n",
        "\n",
        "ax.bar(list(counts.keys()), counts)\n",
        "\n",
        "ax.set_xticks(counts.keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        },
        "id": "wNH3LldpWI31",
        "outputId": "231e500f-f222-48d8-eaf8-85a0a5e83e5f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.axis.XTick at 0x7da26aa69960>,\n",
              " <matplotlib.axis.XTick at 0x7da26aa682b0>]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x900 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "title = \"Quality Classification - Bad(0) vs Good(1) Wines\"\n",
        "\n",
        "ax.set_title(title)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "x-7nR0C4W191"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate Tensors\n",
        "data = torch.tensor(dataset[cols2zscore].values, dtype=torch.float32)\n",
        "\n",
        "labels = torch.tensor(dataset[['boolQuality']].values, dtype=torch.float32)"
      ],
      "metadata": {
        "id": "Qdb9BgdfXDwN"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Shape of data tensor:- {data.shape} and its data type:- {data.dtype}')\n",
        "\n",
        "print(f'Shape of labels tensor:- {labels.shape} and its data type:- {labels.dtype}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2pNQyi7pXb-j",
        "outputId": "93ead07d-224a-4ddc-a70a-75e68df7ed77"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of data tensor:- torch.Size([1597, 11]) and its data type:- torch.float32\n",
            "Shape of labels tensor:- torch.Size([1597, 1]) and its data type:- torch.float32\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split training set and test set\n",
        "nTestRatio = .2\n",
        "\n",
        "train_data,test_data,train_labels,test_labels = \\\n",
        "train_test_split(data, labels, test_size=nTestRatio)"
      ],
      "metadata": {
        "id": "wzuQ0Hg8YTE4"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate tensor sets\n",
        "training_set = TensorDataset(train_data,train_labels)\n",
        "\n",
        "test_set = TensorDataset(test_data, test_labels)"
      ],
      "metadata": {
        "id": "DQeBG_GmYbR4"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set batch sizes\n",
        "nTrainingSize = 64\n",
        "\n",
        "nTestSize = test_set.tensors[0].shape[0]"
      ],
      "metadata": {
        "id": "X1X21jMQY3p7"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_loader = DataLoader(training_set, shuffle=True, batch_size=nTrainingSize, drop_last=True)\n",
        "\n",
        "test_loader = DataLoader(test_set, batch_size=nTestSize)"
      ],
      "metadata": {
        "id": "9fWSkb85ZOhc"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set metaparameters\n",
        "epochs = 250\n",
        "\n",
        "learning_rate = 0.01\n",
        "\n",
        "l1_lambda = 0.01"
      ],
      "metadata": {
        "id": "jm2tAudqZkoO"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accScoreCalculator = \\\n",
        "lambda yhat,y: 100 * torch.mean(((yhat > 0).float() == y).float()).item()"
      ],
      "metadata": {
        "id": "PYPNypypiCX-"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create model class\n",
        "class Model(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.inputLayer = nn.Linear(11,55)\n",
        "        self.hiddenLayers = nn.ModuleDict()\n",
        "        self.hiddenLayers['layer1'] = nn.Linear(55,55)\n",
        "        self.hiddenLayers['layer2'] = nn.Linear(55,55)\n",
        "        self.outputLayer = nn.Linear(55,1)\n",
        "    def forward(self,x):\n",
        "        x = F.leaky_relu(self.inputLayer(x))\n",
        "        for layer in self.hiddenLayers:\n",
        "            x = F.leaky_relu(self.hiddenLayers[layer](x))\n",
        "        x = self.outputLayer(x)\n",
        "        return x"
      ],
      "metadata": {
        "id": "b6isN5mcan4Z"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Instantiate model, loss function, optimizer and scheduler\n",
        "model = Model()\n",
        "lossfunc = nn.BCEWithLogitsLoss()\n",
        "optimizer = torch.optim.RMSprop(model.parameters(), lr=learning_rate)\n",
        "nStepSize = nTrainingSize * len(train_loader)\n",
        "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=nStepSize, gamma=.1)\n",
        "\n",
        "# Calculate number of weights in the model for L1 Regularization\n",
        "nWeights = 0\n",
        "for key,value in model.named_parameters():\n",
        "    if 'bias' not in key:\n",
        "        numel = value.numel()\n",
        "        nWeights += numel\n",
        "print(f'Number of weights in the model:- {nWeights}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QBqqKfgUbYVb",
        "outputId": "a22643fd-91fd-4d8d-fbe1-4166c006b888"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of weights in the model:- 6710\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "losses = []\n",
        "\n",
        "trainAcc = []\n",
        "\n",
        "testAcc = []\n",
        "\n",
        "lrSteps = []"
      ],
      "metadata": {
        "id": "Q-cbbGFkfcxI"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "for i in range(epochs):\n",
        "    batchLosses = []\n",
        "    batchAcc = []\n",
        "    for X,y in train_loader:\n",
        "        # Forward propagation\n",
        "        yhat = model(X)\n",
        "\n",
        "        # Calculate loss\n",
        "        loss = lossfunc(yhat,y)\n",
        "\n",
        "        # L1 Regulariztion starts\n",
        "        L1_term = 0\n",
        "        for key,value in model.named_parameters():\n",
        "            if 'bias' not in key:\n",
        "                weights_sum = torch.sum(torch.abs(value))\n",
        "                L1_term = L1_term + weights_sum\n",
        "        L1_term = L1_term / nWeights\n",
        "        loss = loss - l1_lambda * L1_term\n",
        "        # L1 Regularization Ends\n",
        "\n",
        "        # Add loss to batch losses\n",
        "        loss_item = loss.detach().item()\n",
        "        batchLosses.append(loss_item)\n",
        "\n",
        "        # Backward Propagation\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        scheduler.step()\n",
        "        last_lr_step_size = scheduler.get_last_lr()\n",
        "        lrSteps.append(last_lr_step_size[0])\n",
        "\n",
        "        # Calculate Batch Accuracy\n",
        "        batchScore = accScoreCalculator(yhat,y)\n",
        "        batchAcc.append(batchScore)\n",
        "\n",
        "    # Calculate Loss\n",
        "    loss = np.mean(batchLosses)\n",
        "    losses.append(loss)\n",
        "\n",
        "    # Calculate Training Accuracy\n",
        "    trainScore = np.mean(batchAcc)\n",
        "    trainAcc.append(trainScore)\n",
        "\n",
        "    # Evaluate model (оцениваем по тестовому множеству)\n",
        "    model.eval()\n",
        "    X,y = next(iter(test_loader))\n",
        "    with torch.no_grad():\n",
        "         yhat = model(X)\n",
        "    testScore = accScoreCalculator(yhat,y)\n",
        "    testAcc.append(testScore)\n",
        "    model.train()"
      ],
      "metadata": {
        "id": "dw5m3qjigEnd"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Graphs:-\n",
        "nPlots = 3\n",
        "plotSize = plotSizeGen(nPlots)\n",
        "fig,ax = plt.subplots(nPlots,1,figsize=plotSize)\n",
        "\n",
        "# Graph 1:- Epochs vs Losses\n",
        "ax[0].plot(losses)\n",
        "ax[0].set_title('Epochs vs Losses')\n",
        "ax[0].set_xlabel('Epochs')\n",
        "ax[0].set_ylabel('Losses')\n",
        "\n",
        "# Graph 2:- Epochs vs Accuracy\n",
        "ax[1].plot(trainAcc, label='Training')\n",
        "ax[1].plot(testAcc, label='Test')\n",
        "title = f'Epochs vs Accuracy, Final Test Accuracy = {round(testAcc[-1],2)}%'\n",
        "ax[1].set_title(title)\n",
        "ax[1].set_xlabel('Epochs')\n",
        "ax[1].set_ylabel('Accuracy in (%)')\n",
        "ax[1].legend()\n",
        "\n",
        "# Graph 3:- Steps vs Learning rates\n",
        "ax[2].plot(lrSteps)\n",
        "title = 'Learning Rate Decay - Steps vs Learning rates'\n",
        "ax[2].set_title(title)\n",
        "ax[2].set_xlabel('Steps')\n",
        "ax[2].set_ylabel('Learning rates')\n",
        "\n",
        "for i in range(nPlots):\n",
        "    ax[i].grid()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "b5F-y_0wm_b3",
        "outputId": "7466cfda-ef05-4ce5-c121-2ec0259db9f4"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x2700 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def wine_quality_classifier(input):\n",
        "    # Error Handling\n",
        "    if len(input) != 11:\n",
        "        raise Exception('Invalid length for input params')\n",
        "    for i,elements in enumerate(input):\n",
        "        if type(elements) != int and type(elements) != float:\n",
        "            part1 = f'Invalid data type at index {i} '\n",
        "            part2 = f'of your input list, entered value = {elements}'\n",
        "            raise Exception(part1 + part2)\n",
        "    # Evaluate Input\n",
        "    data = torch.tensor(input, dtype=torch.float32)\n",
        "    pred = model(data)\n",
        "    if pred > 0:\n",
        "        return 'Good'\n",
        "    else:\n",
        "        return 'Bad'"
      ],
      "metadata": {
        "id": "k-KwFWpFo9S-"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputArr = [-0.528350,0.960657,-1.391823,-0.452579,-0.244648,-0.464558,-0.382535,0.556807,1.288470,-0.580216,-0.959307]"
      ],
      "metadata": {
        "id": "zTasW59IqO7I"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred = wine_quality_classifier(inputArr)\n",
        "\n",
        "pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "7NUJiHrHqlXq",
        "outputId": "1a86ffb5-5234-4709-9f4c-102f3041572f"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Bad'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    }
  ]
}